{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## This is the in-class dem for the Pandas lesson"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "surveys_df = pd.read_csv(\"surveys.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "record_id            int64\n",
       "month                int64\n",
       "day                  int64\n",
       "year                 int64\n",
       "plot_id              int64\n",
       "species_id          object\n",
       "sex                 object\n",
       "hindfoot_length    float64\n",
       "weight             float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "surveys_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['record_id', 'month', 'day', 'year', 'plot_id', 'species_id', 'sex',\n",
       "       'hindfoot_length', 'weight'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "surveys_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(35549, 9)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "surveys_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pandas-lesson.ipynb  species.csv          surveys.csv\r\n"
     ]
    }
   ],
   "source": [
    "ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['record_id', 'month', 'day', 'year', 'plot_id', 'species_id', 'sex',\n",
       "       'hindfoot_length', 'weight'], dtype=object)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "surveys_df.columns.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['NL', 'DM', 'PF', 'PE', 'DS', 'PP', 'SH', 'OT', 'DO', 'OX', 'SS',\n",
       "       'OL', 'RM', nan, 'SA', 'PM', 'AH', 'DX', 'AB', 'CB', 'CM', 'CQ',\n",
       "       'RF', 'PC', 'PG', 'PH', 'PU', 'CV', 'UR', 'UP', 'ZL', 'UL', 'CS',\n",
       "       'SC', 'BA', 'SF', 'RO', 'AS', 'SO', 'PI', 'ST', 'CU', 'SU', 'RX',\n",
       "       'PB', 'PL', 'PX', 'CT', 'US'], dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.unique(surveys_df['species_id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         2\n",
       "1         3\n",
       "2         2\n",
       "3         7\n",
       "4         3\n",
       "5         1\n",
       "6         2\n",
       "7         1\n",
       "8         1\n",
       "9         6\n",
       "10        5\n",
       "11        7\n",
       "12        3\n",
       "13        8\n",
       "14        6\n",
       "15        4\n",
       "16        3\n",
       "17        2\n",
       "18        4\n",
       "19       11\n",
       "20       14\n",
       "21       15\n",
       "22       13\n",
       "23       13\n",
       "24        9\n",
       "25       15\n",
       "26       15\n",
       "27       11\n",
       "28       11\n",
       "29       10\n",
       "         ..\n",
       "35519     9\n",
       "35520     9\n",
       "35521     9\n",
       "35522     9\n",
       "35523     9\n",
       "35524     9\n",
       "35525     8\n",
       "35526    13\n",
       "35527    13\n",
       "35528    13\n",
       "35529    13\n",
       "35530    13\n",
       "35531    14\n",
       "35532    14\n",
       "35533    14\n",
       "35534    14\n",
       "35535    14\n",
       "35536    14\n",
       "35537    15\n",
       "35538    15\n",
       "35539    15\n",
       "35540    15\n",
       "35541    15\n",
       "35542    15\n",
       "35543    15\n",
       "35544    15\n",
       "35545    15\n",
       "35546    10\n",
       "35547     7\n",
       "35548     5\n",
       "Name: plot_id, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "surveys_df['plot_id']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "records_unique = pd.unique(surveys_df['plot_id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2,  3,  7,  1,  6,  5,  8,  4, 11, 14, 15, 13,  9, 10, 17, 16, 20,\n",
       "       23, 18, 21, 22, 19, 12, 24])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "records_unique"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(records_unique)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "surveys_df['plot_id'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    32283.000000\n",
       "mean        42.672428\n",
       "std         36.631259\n",
       "min          4.000000\n",
       "25%         20.000000\n",
       "50%         37.000000\n",
       "75%         48.000000\n",
       "max        280.000000\n",
       "Name: weight, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "surveys_df['weight'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sorted_data = surveys_df.groupby('sex')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.DataFrameGroupBy object at 0x1175afd30>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>day</th>\n",
       "      <th>hindfoot_length</th>\n",
       "      <th>month</th>\n",
       "      <th>plot_id</th>\n",
       "      <th>record_id</th>\n",
       "      <th>weight</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">F</th>\n",
       "      <th>count</th>\n",
       "      <td>15690.000000</td>\n",
       "      <td>14894.000000</td>\n",
       "      <td>15690.000000</td>\n",
       "      <td>15690.000000</td>\n",
       "      <td>15690.000000</td>\n",
       "      <td>15303.000000</td>\n",
       "      <td>15690.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>16.007138</td>\n",
       "      <td>28.836780</td>\n",
       "      <td>6.583047</td>\n",
       "      <td>11.440854</td>\n",
       "      <td>18036.412046</td>\n",
       "      <td>42.170555</td>\n",
       "      <td>1990.644997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.271144</td>\n",
       "      <td>9.463789</td>\n",
       "      <td>3.367350</td>\n",
       "      <td>6.870684</td>\n",
       "      <td>10423.089000</td>\n",
       "      <td>36.847958</td>\n",
       "      <td>7.598725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1977.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>8917.500000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>1984.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>16.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>18075.500000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>1990.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>23.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>27250.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>1997.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>31.000000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>35547.000000</td>\n",
       "      <td>274.000000</td>\n",
       "      <td>2002.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">M</th>\n",
       "      <th>count</th>\n",
       "      <td>17348.000000</td>\n",
       "      <td>16476.000000</td>\n",
       "      <td>17348.000000</td>\n",
       "      <td>17348.000000</td>\n",
       "      <td>17348.000000</td>\n",
       "      <td>16879.000000</td>\n",
       "      <td>17348.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>16.184286</td>\n",
       "      <td>29.709578</td>\n",
       "      <td>6.392668</td>\n",
       "      <td>11.098282</td>\n",
       "      <td>17754.835601</td>\n",
       "      <td>42.995379</td>\n",
       "      <td>1990.480401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.199274</td>\n",
       "      <td>9.629246</td>\n",
       "      <td>3.420806</td>\n",
       "      <td>6.728713</td>\n",
       "      <td>10132.203323</td>\n",
       "      <td>36.184981</td>\n",
       "      <td>7.403655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1977.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>8969.750000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>1984.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>16.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>17727.500000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>1990.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>23.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>26454.250000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>1997.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>31.000000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>35548.000000</td>\n",
       "      <td>280.000000</td>\n",
       "      <td>2002.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    day  hindfoot_length         month       plot_id  \\\n",
       "sex                                                                    \n",
       "F   count  15690.000000     14894.000000  15690.000000  15690.000000   \n",
       "    mean      16.007138        28.836780      6.583047     11.440854   \n",
       "    std        8.271144         9.463789      3.367350      6.870684   \n",
       "    min        1.000000         7.000000      1.000000      1.000000   \n",
       "    25%        9.000000        21.000000      4.000000      5.000000   \n",
       "    50%       16.000000        27.000000      7.000000     12.000000   \n",
       "    75%       23.000000        36.000000     10.000000     17.000000   \n",
       "    max       31.000000        64.000000     12.000000     24.000000   \n",
       "M   count  17348.000000     16476.000000  17348.000000  17348.000000   \n",
       "    mean      16.184286        29.709578      6.392668     11.098282   \n",
       "    std        8.199274         9.629246      3.420806      6.728713   \n",
       "    min        1.000000         2.000000      1.000000      1.000000   \n",
       "    25%        9.000000        21.000000      3.000000      5.000000   \n",
       "    50%       16.000000        34.000000      6.000000     11.000000   \n",
       "    75%       23.000000        36.000000      9.000000     17.000000   \n",
       "    max       31.000000        58.000000     12.000000     24.000000   \n",
       "\n",
       "              record_id        weight          year  \n",
       "sex                                                  \n",
       "F   count  15690.000000  15303.000000  15690.000000  \n",
       "    mean   18036.412046     42.170555   1990.644997  \n",
       "    std    10423.089000     36.847958      7.598725  \n",
       "    min        3.000000      4.000000   1977.000000  \n",
       "    25%     8917.500000     20.000000   1984.000000  \n",
       "    50%    18075.500000     34.000000   1990.000000  \n",
       "    75%    27250.000000     46.000000   1997.000000  \n",
       "    max    35547.000000    274.000000   2002.000000  \n",
       "M   count  17348.000000  16879.000000  17348.000000  \n",
       "    mean   17754.835601     42.995379   1990.480401  \n",
       "    std    10132.203323     36.184981      7.403655  \n",
       "    min        1.000000      4.000000   1977.000000  \n",
       "    25%     8969.750000     20.000000   1984.000000  \n",
       "    50%    17727.500000     39.000000   1990.000000  \n",
       "    75%    26454.250000     49.000000   1997.000000  \n",
       "    max    35548.000000    280.000000   2002.000000  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>record_id</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>year</th>\n",
       "      <th>plot_id</th>\n",
       "      <th>hindfoot_length</th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <td>18036.412046</td>\n",
       "      <td>6.583047</td>\n",
       "      <td>16.007138</td>\n",
       "      <td>1990.644997</td>\n",
       "      <td>11.440854</td>\n",
       "      <td>28.836780</td>\n",
       "      <td>42.170555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>17754.835601</td>\n",
       "      <td>6.392668</td>\n",
       "      <td>16.184286</td>\n",
       "      <td>1990.480401</td>\n",
       "      <td>11.098282</td>\n",
       "      <td>29.709578</td>\n",
       "      <td>42.995379</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        record_id     month        day         year    plot_id  \\\n",
       "sex                                                              \n",
       "F    18036.412046  6.583047  16.007138  1990.644997  11.440854   \n",
       "M    17754.835601  6.392668  16.184286  1990.480401  11.098282   \n",
       "\n",
       "     hindfoot_length     weight  \n",
       "sex                              \n",
       "F          28.836780  42.170555  \n",
       "M          29.709578  42.995379  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted_data.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "      <th>year</th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "  <tbody>\n",
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       "      <th>F</th>\n",
       "      <td>15690</td>\n",
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       "      <td>15690</td>\n",
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       "      <th>M</th>\n",
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      "text/plain": [
       "     record_id  month    day   year  plot_id  species_id  hindfoot_length  \\\n",
       "sex                                                                         \n",
       "F        15690  15690  15690  15690    15690       15690            14894   \n",
       "M        17348  17348  17348  17348    17348       17348            16476   \n",
       "\n",
       "     weight  \n",
       "sex          \n",
       "F     15303  \n",
       "M     16879  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "sorted_data.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "sorted_data2 = surveys_df.groupby(['plot_id','sex'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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       "      <td>30.161220</td>\n",
       "      <td>52.561845</td>\n",
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       "      <td>16.507767</td>\n",
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       "      <td>34.097959</td>\n",
       "      <td>48.888119</td>\n",
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       "      <td>6.142315</td>\n",
       "      <td>15.722960</td>\n",
       "      <td>1986.485769</td>\n",
       "      <td>28.921844</td>\n",
       "      <td>40.974806</td>\n",
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       "      <td>15.703072</td>\n",
       "      <td>1986.817406</td>\n",
       "      <td>29.694794</td>\n",
       "      <td>40.708551</td>\n",
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       "      <td>26.981322</td>\n",
       "      <td>36.352288</td>\n",
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       "      <td>17849.574607</td>\n",
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       "      <td>16.091623</td>\n",
       "      <td>1990.556283</td>\n",
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       "      <td>36.867388</td>\n",
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       "      <td>15.313433</td>\n",
       "      <td>1991.441791</td>\n",
       "      <td>19.779553</td>\n",
       "      <td>20.006135</td>\n",
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       "      <th>M</th>\n",
       "      <td>19188.729642</td>\n",
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       "      <td>32.187578</td>\n",
       "      <td>45.623011</td>\n",
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       "      <td>1991.686673</td>\n",
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       "      <td>35.126092</td>\n",
       "      <td>53.618469</td>\n",
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       "      <td>15.209163</td>\n",
       "      <td>1990.632470</td>\n",
       "      <td>34.175732</td>\n",
       "      <td>49.519309</td>\n",
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       "      <th rowspan=\"2\" valign=\"top\">10</th>\n",
       "      <th>F</th>\n",
       "      <td>16001.496454</td>\n",
       "      <td>5.588652</td>\n",
       "      <td>16.964539</td>\n",
       "      <td>1989.248227</td>\n",
       "      <td>18.641791</td>\n",
       "      <td>17.094203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>15708.704225</td>\n",
       "      <td>5.718310</td>\n",
       "      <td>16.739437</td>\n",
       "      <td>1989.007042</td>\n",
       "      <td>19.567164</td>\n",
       "      <td>19.971223</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">11</th>\n",
       "      <th>F</th>\n",
       "      <td>16994.962287</td>\n",
       "      <td>6.759124</td>\n",
       "      <td>16.283455</td>\n",
       "      <td>1989.836983</td>\n",
       "      <td>32.029299</td>\n",
       "      <td>43.515075</td>\n",
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       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>16933.909621</td>\n",
       "      <td>6.374150</td>\n",
       "      <td>15.974733</td>\n",
       "      <td>1989.856171</td>\n",
       "      <td>32.078014</td>\n",
       "      <td>43.366197</td>\n",
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       "      <th rowspan=\"2\" valign=\"top\">12</th>\n",
       "      <th>F</th>\n",
       "      <td>17457.966981</td>\n",
       "      <td>6.509434</td>\n",
       "      <td>16.305660</td>\n",
       "      <td>1990.266981</td>\n",
       "      <td>30.975124</td>\n",
       "      <td>49.831731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>17592.327500</td>\n",
       "      <td>6.304167</td>\n",
       "      <td>16.367500</td>\n",
       "      <td>1990.400833</td>\n",
       "      <td>31.762489</td>\n",
       "      <td>48.909710</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">13</th>\n",
       "      <th>F</th>\n",
       "      <td>18033.100318</td>\n",
       "      <td>6.802548</td>\n",
       "      <td>16.229299</td>\n",
       "      <td>1990.619427</td>\n",
       "      <td>27.201014</td>\n",
       "      <td>40.524590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>16969.044700</td>\n",
       "      <td>6.480204</td>\n",
       "      <td>16.005109</td>\n",
       "      <td>1989.911877</td>\n",
       "      <td>27.893793</td>\n",
       "      <td>40.097754</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">14</th>\n",
       "      <th>F</th>\n",
       "      <td>17097.145275</td>\n",
       "      <td>6.510578</td>\n",
       "      <td>16.681241</td>\n",
       "      <td>1989.974612</td>\n",
       "      <td>32.973373</td>\n",
       "      <td>47.355491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>17891.948598</td>\n",
       "      <td>6.660748</td>\n",
       "      <td>16.504673</td>\n",
       "      <td>1990.587850</td>\n",
       "      <td>32.961802</td>\n",
       "      <td>45.159378</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">15</th>\n",
       "      <th>F</th>\n",
       "      <td>20602.449064</td>\n",
       "      <td>6.569647</td>\n",
       "      <td>16.162162</td>\n",
       "      <td>1992.523909</td>\n",
       "      <td>21.949891</td>\n",
       "      <td>26.670236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>18104.019560</td>\n",
       "      <td>6.185819</td>\n",
       "      <td>17.413203</td>\n",
       "      <td>1990.770171</td>\n",
       "      <td>21.803109</td>\n",
       "      <td>27.523691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">16</th>\n",
       "      <th>F</th>\n",
       "      <td>19002.445946</td>\n",
       "      <td>6.360360</td>\n",
       "      <td>16.819820</td>\n",
       "      <td>1991.351351</td>\n",
       "      <td>23.144928</td>\n",
       "      <td>25.810427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>18434.714286</td>\n",
       "      <td>6.201465</td>\n",
       "      <td>16.622711</td>\n",
       "      <td>1990.926740</td>\n",
       "      <td>23.480916</td>\n",
       "      <td>23.811321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">17</th>\n",
       "      <th>F</th>\n",
       "      <td>18234.322870</td>\n",
       "      <td>6.650224</td>\n",
       "      <td>15.892377</td>\n",
       "      <td>1990.785874</td>\n",
       "      <td>30.918536</td>\n",
       "      <td>48.176201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>18857.651472</td>\n",
       "      <td>6.569801</td>\n",
       "      <td>16.183286</td>\n",
       "      <td>1991.331434</td>\n",
       "      <td>32.227634</td>\n",
       "      <td>47.558853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">18</th>\n",
       "      <th>F</th>\n",
       "      <td>17940.875497</td>\n",
       "      <td>6.698013</td>\n",
       "      <td>15.960265</td>\n",
       "      <td>1990.536424</td>\n",
       "      <td>26.690341</td>\n",
       "      <td>36.963514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>15106.718850</td>\n",
       "      <td>6.610224</td>\n",
       "      <td>16.797125</td>\n",
       "      <td>1988.551118</td>\n",
       "      <td>27.703072</td>\n",
       "      <td>43.546952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">19</th>\n",
       "      <th>F</th>\n",
       "      <td>21848.216475</td>\n",
       "      <td>6.701149</td>\n",
       "      <td>15.226054</td>\n",
       "      <td>1993.417625</td>\n",
       "      <td>21.257937</td>\n",
       "      <td>21.978599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>19470.779690</td>\n",
       "      <td>6.533563</td>\n",
       "      <td>16.647160</td>\n",
       "      <td>1991.740103</td>\n",
       "      <td>21.071685</td>\n",
       "      <td>20.306878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">20</th>\n",
       "      <th>F</th>\n",
       "      <td>17510.769231</td>\n",
       "      <td>6.743077</td>\n",
       "      <td>16.026154</td>\n",
       "      <td>1990.253846</td>\n",
       "      <td>27.069193</td>\n",
       "      <td>52.624406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>16076.192496</td>\n",
       "      <td>6.489396</td>\n",
       "      <td>16.375204</td>\n",
       "      <td>1989.243067</td>\n",
       "      <td>27.908451</td>\n",
       "      <td>44.197279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">21</th>\n",
       "      <th>F</th>\n",
       "      <td>22452.636661</td>\n",
       "      <td>6.860884</td>\n",
       "      <td>16.307692</td>\n",
       "      <td>1993.878887</td>\n",
       "      <td>22.366554</td>\n",
       "      <td>25.974832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>20120.399113</td>\n",
       "      <td>6.671840</td>\n",
       "      <td>16.203991</td>\n",
       "      <td>1992.199557</td>\n",
       "      <td>21.736721</td>\n",
       "      <td>22.772622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">22</th>\n",
       "      <th>F</th>\n",
       "      <td>18499.695976</td>\n",
       "      <td>6.651267</td>\n",
       "      <td>15.521610</td>\n",
       "      <td>1990.973174</td>\n",
       "      <td>34.108320</td>\n",
       "      <td>53.647059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>18015.365527</td>\n",
       "      <td>6.381872</td>\n",
       "      <td>16.682021</td>\n",
       "      <td>1990.650817</td>\n",
       "      <td>33.359746</td>\n",
       "      <td>54.572531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">23</th>\n",
       "      <th>F</th>\n",
       "      <td>15863.193939</td>\n",
       "      <td>6.860606</td>\n",
       "      <td>16.036364</td>\n",
       "      <td>1989.024242</td>\n",
       "      <td>20.051948</td>\n",
       "      <td>20.564417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>17091.338164</td>\n",
       "      <td>6.391304</td>\n",
       "      <td>16.077295</td>\n",
       "      <td>1989.961353</td>\n",
       "      <td>19.850000</td>\n",
       "      <td>18.941463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">24</th>\n",
       "      <th>F</th>\n",
       "      <td>13702.224280</td>\n",
       "      <td>6.596708</td>\n",
       "      <td>16.393004</td>\n",
       "      <td>1987.485597</td>\n",
       "      <td>26.993377</td>\n",
       "      <td>47.914405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>15208.136082</td>\n",
       "      <td>6.360825</td>\n",
       "      <td>16.971134</td>\n",
       "      <td>1988.641237</td>\n",
       "      <td>25.786996</td>\n",
       "      <td>39.321503</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                record_id     month        day         year  hindfoot_length  \\\n",
       "plot_id sex                                                                    \n",
       "1       F    18390.384434  6.597877  15.338443  1990.933962        31.733911   \n",
       "        M    17197.740639  6.121461  15.905936  1990.091324        34.302770   \n",
       "2       F    17714.753608  6.426804  16.288660  1990.449485        30.161220   \n",
       "        M    18085.458042  6.340035  15.440559  1990.756119        30.353760   \n",
       "3       F    19888.783875  6.604703  16.161254  1992.013438        23.774044   \n",
       "        M    20226.767857  6.271429  16.450000  1992.275000        23.833744   \n",
       "4       F    17489.205275  6.442661  15.746560  1990.235092        33.249102   \n",
       "        M    18493.841748  6.430097  16.507767  1991.000971        34.097959   \n",
       "5       F    12280.793169  6.142315  15.722960  1986.485769        28.921844   \n",
       "        M    12798.426621  6.194539  15.703072  1986.817406        29.694794   \n",
       "6       F    19406.503392  6.628223  16.313433  1991.579376        26.981322   \n",
       "        M    17849.574607  6.035340  16.091623  1990.556283        27.425591   \n",
       "7       F    19069.668657  6.385075  15.313433  1991.441791        19.779553   \n",
       "        M    19188.729642  6.719870  15.778502  1991.462541        20.536667   \n",
       "8       F    18920.276190  6.632143  15.836905  1991.267857        32.187578   \n",
       "        M    19452.109868  6.571719  15.854527  1991.686673        33.751059   \n",
       "9       F    16217.497069  6.499414  15.555686  1989.303634        35.126092   \n",
       "        M    18000.710159  6.361554  15.209163  1990.632470        34.175732   \n",
       "10      F    16001.496454  5.588652  16.964539  1989.248227        18.641791   \n",
       "        M    15708.704225  5.718310  16.739437  1989.007042        19.567164   \n",
       "11      F    16994.962287  6.759124  16.283455  1989.836983        32.029299   \n",
       "        M    16933.909621  6.374150  15.974733  1989.856171        32.078014   \n",
       "12      F    17457.966981  6.509434  16.305660  1990.266981        30.975124   \n",
       "        M    17592.327500  6.304167  16.367500  1990.400833        31.762489   \n",
       "13      F    18033.100318  6.802548  16.229299  1990.619427        27.201014   \n",
       "        M    16969.044700  6.480204  16.005109  1989.911877        27.893793   \n",
       "14      F    17097.145275  6.510578  16.681241  1989.974612        32.973373   \n",
       "        M    17891.948598  6.660748  16.504673  1990.587850        32.961802   \n",
       "15      F    20602.449064  6.569647  16.162162  1992.523909        21.949891   \n",
       "        M    18104.019560  6.185819  17.413203  1990.770171        21.803109   \n",
       "16      F    19002.445946  6.360360  16.819820  1991.351351        23.144928   \n",
       "        M    18434.714286  6.201465  16.622711  1990.926740        23.480916   \n",
       "17      F    18234.322870  6.650224  15.892377  1990.785874        30.918536   \n",
       "        M    18857.651472  6.569801  16.183286  1991.331434        32.227634   \n",
       "18      F    17940.875497  6.698013  15.960265  1990.536424        26.690341   \n",
       "        M    15106.718850  6.610224  16.797125  1988.551118        27.703072   \n",
       "19      F    21848.216475  6.701149  15.226054  1993.417625        21.257937   \n",
       "        M    19470.779690  6.533563  16.647160  1991.740103        21.071685   \n",
       "20      F    17510.769231  6.743077  16.026154  1990.253846        27.069193   \n",
       "        M    16076.192496  6.489396  16.375204  1989.243067        27.908451   \n",
       "21      F    22452.636661  6.860884  16.307692  1993.878887        22.366554   \n",
       "        M    20120.399113  6.671840  16.203991  1992.199557        21.736721   \n",
       "22      F    18499.695976  6.651267  15.521610  1990.973174        34.108320   \n",
       "        M    18015.365527  6.381872  16.682021  1990.650817        33.359746   \n",
       "23      F    15863.193939  6.860606  16.036364  1989.024242        20.051948   \n",
       "        M    17091.338164  6.391304  16.077295  1989.961353        19.850000   \n",
       "24      F    13702.224280  6.596708  16.393004  1987.485597        26.993377   \n",
       "        M    15208.136082  6.360825  16.971134  1988.641237        25.786996   \n",
       "\n",
       "                weight  \n",
       "plot_id sex             \n",
       "1       F    46.311138  \n",
       "        M    55.950560  \n",
       "2       F    52.561845  \n",
       "        M    51.391382  \n",
       "3       F    31.215349  \n",
       "        M    34.163241  \n",
       "4       F    46.818824  \n",
       "        M    48.888119  \n",
       "5       F    40.974806  \n",
       "        M    40.708551  \n",
       "6       F    36.352288  \n",
       "        M    36.867388  \n",
       "7       F    20.006135  \n",
       "        M    21.194719  \n",
       "8       F    45.623011  \n",
       "        M    49.641372  \n",
       "9       F    53.618469  \n",
       "        M    49.519309  \n",
       "10      F    17.094203  \n",
       "        M    19.971223  \n",
       "11      F    43.515075  \n",
       "        M    43.366197  \n",
       "12      F    49.831731  \n",
       "        M    48.909710  \n",
       "13      F    40.524590  \n",
       "        M    40.097754  \n",
       "14      F    47.355491  \n",
       "        M    45.159378  \n",
       "15      F    26.670236  \n",
       "        M    27.523691  \n",
       "16      F    25.810427  \n",
       "        M    23.811321  \n",
       "17      F    48.176201  \n",
       "        M    47.558853  \n",
       "18      F    36.963514  \n",
       "        M    43.546952  \n",
       "19      F    21.978599  \n",
       "        M    20.306878  \n",
       "20      F    52.624406  \n",
       "        M    44.197279  \n",
       "21      F    25.974832  \n",
       "        M    22.772622  \n",
       "22      F    53.647059  \n",
       "        M    54.572531  \n",
       "23      F    20.564417  \n",
       "        M    18.941463  \n",
       "24      F    47.914405  \n",
       "        M    39.321503  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted_data2.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "by_plot = surveys_df.groupby('plot_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "plot_id       \n",
       "1        count    1903.000000\n",
       "         mean       51.822911\n",
       "         std        38.176670\n",
       "         min         4.000000\n",
       "         25%        30.000000\n",
       "         50%        44.000000\n",
       "         75%        53.000000\n",
       "         max       231.000000\n",
       "2        count    2074.000000\n",
       "         mean       52.251688\n",
       "         std        46.503602\n",
       "         min         5.000000\n",
       "         25%        24.000000\n",
       "         50%        41.000000\n",
       "         75%        50.000000\n",
       "         max       278.000000\n",
       "3        count    1710.000000\n",
       "         mean       32.654386\n",
       "         std        35.641630\n",
       "         min         4.000000\n",
       "         25%        14.000000\n",
       "         50%        23.000000\n",
       "         75%        36.000000\n",
       "         max       250.000000\n",
       "4        count    1866.000000\n",
       "         mean       47.928189\n",
       "         std        32.886598\n",
       "         min         4.000000\n",
       "         25%        30.000000\n",
       "         50%        43.000000\n",
       "                     ...     \n",
       "21       std        21.199819\n",
       "         min         4.000000\n",
       "         25%        10.000000\n",
       "         50%        22.000000\n",
       "         75%        31.000000\n",
       "         max       190.000000\n",
       "22       count    1298.000000\n",
       "         mean       54.146379\n",
       "         std        38.743967\n",
       "         min         5.000000\n",
       "         25%        29.000000\n",
       "         50%        42.000000\n",
       "         75%        54.000000\n",
       "         max       212.000000\n",
       "23       count     369.000000\n",
       "         mean       19.634146\n",
       "         std        18.382678\n",
       "         min         4.000000\n",
       "         25%        10.000000\n",
       "         50%        14.000000\n",
       "         75%        23.000000\n",
       "         max       199.000000\n",
       "24       count     960.000000\n",
       "         mean       43.679167\n",
       "         std        45.936588\n",
       "         min         4.000000\n",
       "         25%        19.000000\n",
       "         50%        27.500000\n",
       "         75%        45.000000\n",
       "         max       251.000000\n",
       "Name: weight, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "by_plot['weight'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "species_counts = surveys_df.groupby('species_id')['record_id'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "species_id\n",
       "AB      303\n",
       "AH      437\n",
       "AS        2\n",
       "BA       46\n",
       "CB       50\n",
       "CM       13\n",
       "CQ       16\n",
       "CS        1\n",
       "CT        1\n",
       "CU        1\n",
       "CV        1\n",
       "DM    10596\n",
       "DO     3027\n",
       "DS     2504\n",
       "DX       40\n",
       "NL     1252\n",
       "OL     1006\n",
       "OT     2249\n",
       "OX       12\n",
       "PB     2891\n",
       "PC       39\n",
       "PE     1299\n",
       "PF     1597\n",
       "PG        8\n",
       "PH       32\n",
       "PI        9\n",
       "PL       36\n",
       "PM      899\n",
       "PP     3123\n",
       "PU        5\n",
       "PX        6\n",
       "RF       75\n",
       "RM     2609\n",
       "RO        8\n",
       "RX        2\n",
       "SA       75\n",
       "SC        1\n",
       "SF       43\n",
       "SH      147\n",
       "SO       43\n",
       "SS      248\n",
       "ST        1\n",
       "SU        5\n",
       "UL        4\n",
       "UP        8\n",
       "UR       10\n",
       "US        4\n",
       "ZL        2\n",
       "Name: record_id, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "species_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "36"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "surveys_df.groupby('species_id')['record_id'].count()['PL']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         NaN\n",
       "1         NaN\n",
       "2         NaN\n",
       "3         NaN\n",
       "4         NaN\n",
       "5         NaN\n",
       "6         NaN\n",
       "7         NaN\n",
       "8         NaN\n",
       "9         NaN\n",
       "10        NaN\n",
       "11        NaN\n",
       "12        NaN\n",
       "13        NaN\n",
       "14        NaN\n",
       "15        NaN\n",
       "16        NaN\n",
       "17        NaN\n",
       "18        NaN\n",
       "19        NaN\n",
       "20        NaN\n",
       "21        NaN\n",
       "22        NaN\n",
       "23        NaN\n",
       "24        NaN\n",
       "25        NaN\n",
       "26        NaN\n",
       "27        NaN\n",
       "28        NaN\n",
       "29        NaN\n",
       "         ... \n",
       "35519    36.0\n",
       "35520    48.0\n",
       "35521    45.0\n",
       "35522    44.0\n",
       "35523    27.0\n",
       "35524    26.0\n",
       "35525    24.0\n",
       "35526    43.0\n",
       "35527     NaN\n",
       "35528    25.0\n",
       "35529     NaN\n",
       "35530     NaN\n",
       "35531    43.0\n",
       "35532    48.0\n",
       "35533    56.0\n",
       "35534    53.0\n",
       "35535    42.0\n",
       "35536    46.0\n",
       "35537    31.0\n",
       "35538    68.0\n",
       "35539    23.0\n",
       "35540    31.0\n",
       "35541    29.0\n",
       "35542    34.0\n",
       "35543     NaN\n",
       "35544     NaN\n",
       "35545     NaN\n",
       "35546    14.0\n",
       "35547    51.0\n",
       "35548     NaN\n",
       "Name: weight, dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "surveys_df['weight']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          NaN\n",
       "1          NaN\n",
       "2          NaN\n",
       "3          NaN\n",
       "4          NaN\n",
       "5          NaN\n",
       "6          NaN\n",
       "7          NaN\n",
       "8          NaN\n",
       "9          NaN\n",
       "10         NaN\n",
       "11         NaN\n",
       "12         NaN\n",
       "13         NaN\n",
       "14         NaN\n",
       "15         NaN\n",
       "16         NaN\n",
       "17         NaN\n",
       "18         NaN\n",
       "19         NaN\n",
       "20         NaN\n",
       "21         NaN\n",
       "22         NaN\n",
       "23         NaN\n",
       "24         NaN\n",
       "25         NaN\n",
       "26         NaN\n",
       "27         NaN\n",
       "28         NaN\n",
       "29         NaN\n",
       "         ...  \n",
       "35519     72.0\n",
       "35520     96.0\n",
       "35521     90.0\n",
       "35522     88.0\n",
       "35523     54.0\n",
       "35524     52.0\n",
       "35525     48.0\n",
       "35526     86.0\n",
       "35527      NaN\n",
       "35528     50.0\n",
       "35529      NaN\n",
       "35530      NaN\n",
       "35531     86.0\n",
       "35532     96.0\n",
       "35533    112.0\n",
       "35534    106.0\n",
       "35535     84.0\n",
       "35536     92.0\n",
       "35537     62.0\n",
       "35538    136.0\n",
       "35539     46.0\n",
       "35540     62.0\n",
       "35541     58.0\n",
       "35542     68.0\n",
       "35543      NaN\n",
       "35544      NaN\n",
       "35545      NaN\n",
       "35546     28.0\n",
       "35547    102.0\n",
       "35548      NaN\n",
       "Name: weight, dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "surveys_df['weight'] * 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          NaN\n",
       "1          NaN\n",
       "2          NaN\n",
       "3          NaN\n",
       "4          NaN\n",
       "5          NaN\n",
       "6          NaN\n",
       "7          NaN\n",
       "8          NaN\n",
       "9          NaN\n",
       "10         NaN\n",
       "11         NaN\n",
       "12         NaN\n",
       "13         NaN\n",
       "14         NaN\n",
       "15         NaN\n",
       "16         NaN\n",
       "17         NaN\n",
       "18         NaN\n",
       "19         NaN\n",
       "20         NaN\n",
       "21         NaN\n",
       "22         NaN\n",
       "23         NaN\n",
       "24         NaN\n",
       "25         NaN\n",
       "26         NaN\n",
       "27         NaN\n",
       "28         NaN\n",
       "29         NaN\n",
       "         ...  \n",
       "35519    144.0\n",
       "35520    168.0\n",
       "35521    162.0\n",
       "35522    160.0\n",
       "35523    126.0\n",
       "35524    124.0\n",
       "35525    120.0\n",
       "35526    158.0\n",
       "35527      NaN\n",
       "35528    122.0\n",
       "35529      NaN\n",
       "35530      NaN\n",
       "35531    158.0\n",
       "35532    168.0\n",
       "35533    184.0\n",
       "35534    178.0\n",
       "35535    156.0\n",
       "35536    164.0\n",
       "35537    134.0\n",
       "35538    208.0\n",
       "35539    118.0\n",
       "35540    134.0\n",
       "35541    130.0\n",
       "35542    140.0\n",
       "35543      NaN\n",
       "35544      NaN\n",
       "35545      NaN\n",
       "35546    100.0\n",
       "35547    174.0\n",
       "35548      NaN\n",
       "Name: weight, dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "surveys_df['weight'] * 2 + 3 * 24"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1194ae4a8>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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3gtQ11ipwDCddVTM/b39YXubnwGE12zyUHoImzQPHssL8ylTyTa/06SGvVcBp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\nD3BtA9pLW8qyAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1186f7160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "species_counts.plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "total_count = surveys_df.groupby('plot_id')['record_id'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11978bb00>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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U3AK8Dziw7XF5H/C1LnWv6DAcC2ztUvf5cn8uoPnB6+eBPfp6LbbV3Uzzxr+4PGbvK/fN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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11760aba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "total_count.plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "by_plot = surveys_df.groupby('plot_id')['weight']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x119879a20>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/CXydHvtw13qd2fLdttvsantrea49v/OxGfQ4DHycpnoDI220GRGuKE/czmkM+EFL3Vco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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119714860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "by_plot.mean().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sort = surveys_df.groupby('sex')['month']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1198e7e48>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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8v4HfBVadYso24IGqerOqXgZmgM1JVgIrquqpqirgPuC6YfuSJI1uLNcckqwF/jJzv/kD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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1199fd828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sort.count().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sex       \n",
       "F    count    15690.000000\n",
       "     mean         6.583047\n",
       "     std          3.367350\n",
       "     min          1.000000\n",
       "     25%          4.000000\n",
       "     50%          7.000000\n",
       "     75%         10.000000\n",
       "     max         12.000000\n",
       "M    count    17348.000000\n",
       "     mean         6.392668\n",
       "     std          3.420806\n",
       "     min          1.000000\n",
       "     25%          3.000000\n",
       "     50%          6.000000\n",
       "     75%          9.000000\n",
       "     max         12.000000\n",
       "Name: month, dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sort.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "by_plot_sex = surveys_df.groupby(['plot_id','sex'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>day</th>\n",
       "      <th>hindfoot_length</th>\n",
       "      <th>month</th>\n",
       "      <th>record_id</th>\n",
       "      <th>weight</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>plot_id</th>\n",
       "      <th>sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"16\" valign=\"top\">1</th>\n",
       "      <th rowspan=\"8\" valign=\"top\">F</th>\n",
       "      <th>count</th>\n",
       "      <td>848.000000</td>\n",
       "      <td>808.000000</td>\n",
       "      <td>848.000000</td>\n",
       "      <td>848.000000</td>\n",
       "      <td>826.000000</td>\n",
       "      <td>848.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>15.338443</td>\n",
       "      <td>31.733911</td>\n",
       "      <td>6.597877</td>\n",
       "      <td>18390.384434</td>\n",
       "      <td>46.311138</td>\n",
       "      <td>1990.933962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.325993</td>\n",
       "      <td>8.894939</td>\n",
       "      <td>3.366246</td>\n",
       "      <td>10469.790852</td>\n",
       "      <td>33.240958</td>\n",
       "      <td>7.678171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1977.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>8783.500000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>1983.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>15.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>19182.500000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>1991.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>22.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>27691.750000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>1998.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>31.000000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>35393.000000</td>\n",
       "      <td>196.000000</td>\n",
       "      <td>2002.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">M</th>\n",
       "      <th>count</th>\n",
       "      <td>1095.000000</td>\n",
       "      <td>1047.000000</td>\n",
       "      <td>1095.000000</td>\n",
       "      <td>1095.000000</td>\n",
       "      <td>1072.000000</td>\n",
       "      <td>1095.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>15.905936</td>\n",
       "      <td>34.302770</td>\n",
       "      <td>6.121461</td>\n",
       "      <td>17197.740639</td>\n",
       "      <td>55.950560</td>\n",
       "      <td>1990.091324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.053257</td>\n",
       "      <td>8.979955</td>\n",
       "      <td>3.418795</td>\n",
       "      <td>9911.570595</td>\n",
       "      <td>41.035686</td>\n",
       "      <td>7.265208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1977.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>8638.000000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>1983.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>16.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>17043.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>1990.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>23.000000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>25251.500000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>1997.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>31.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>35390.000000</td>\n",
       "      <td>231.000000</td>\n",
       "      <td>2002.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"14\" valign=\"top\">2</th>\n",
       "      <th rowspan=\"8\" valign=\"top\">F</th>\n",
       "      <th>count</th>\n",
       "      <td>970.000000</td>\n",
       "      <td>918.000000</td>\n",
       "      <td>970.000000</td>\n",
       "      <td>970.000000</td>\n",
       "      <td>954.000000</td>\n",
       "      <td>970.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>16.288660</td>\n",
       "      <td>30.161220</td>\n",
       "      <td>6.426804</td>\n",
       "      <td>17714.753608</td>\n",
       "      <td>52.561845</td>\n",
       "      <td>1990.449485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.046509</td>\n",
       "      <td>8.677937</td>\n",
       "      <td>3.537694</td>\n",
       "      <td>10300.015076</td>\n",
       "      <td>45.547697</td>\n",
       "      <td>7.519910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1977.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>9580.250000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>1984.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>16.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>18104.500000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>1990.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>23.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>26586.500000</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>1997.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>31.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>35405.000000</td>\n",
       "      <td>274.000000</td>\n",
       "      <td>2002.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">M</th>\n",
       "      <th>count</th>\n",
       "      <td>1144.000000</td>\n",
       "      <td>1077.000000</td>\n",
       "      <td>1144.000000</td>\n",
       "      <td>1144.000000</td>\n",
       "      <td>1114.000000</td>\n",
       "      <td>1144.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>15.440559</td>\n",
       "      <td>30.353760</td>\n",
       "      <td>6.340035</td>\n",
       "      <td>18085.458042</td>\n",
       "      <td>51.391382</td>\n",
       "      <td>1990.756119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.414667</td>\n",
       "      <td>9.016312</td>\n",
       "      <td>3.623430</td>\n",
       "      <td>10555.331260</td>\n",
       "      <td>46.690887</td>\n",
       "      <td>7.714444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1977.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>8653.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>1983.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>15.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>19024.500000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>1991.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"14\" valign=\"top\">23</th>\n",
       "      <th rowspan=\"6\" valign=\"top\">F</th>\n",
       "      <th>std</th>\n",
       "      <td>8.776973</td>\n",
       "      <td>6.455268</td>\n",
       "      <td>3.353006</td>\n",
       "      <td>8854.378716</td>\n",
       "      <td>18.933945</td>\n",
       "      <td>6.454297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1977.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>9536.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>1984.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>15.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>14692.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>1988.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>24.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>21169.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>30.000000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>35489.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>2002.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">M</th>\n",
       "      <th>count</th>\n",
       "      <td>207.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>207.000000</td>\n",
       "      <td>207.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>207.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>16.077295</td>\n",
       "      <td>19.850000</td>\n",
       "      <td>6.391304</td>\n",
       "      <td>17091.338164</td>\n",
       "      <td>18.941463</td>\n",
       "      <td>1989.961353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>7.955203</td>\n",
       "      <td>5.980496</td>\n",
       "      <td>3.543971</td>\n",
       "      <td>8852.413083</td>\n",
       "      <td>17.979740</td>\n",
       "      <td>6.509027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1977.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>11274.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1986.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>16.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>15693.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>1989.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>23.000000</td>\n",
       "      <td>20.250000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>24740.500000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>1996.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>31.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>35282.000000</td>\n",
       "      <td>131.000000</td>\n",
       "      <td>2002.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"16\" valign=\"top\">24</th>\n",
       "      <th rowspan=\"8\" valign=\"top\">F</th>\n",
       "      <th>count</th>\n",
       "      <td>486.000000</td>\n",
       "      <td>453.000000</td>\n",
       "      <td>486.000000</td>\n",
       "      <td>486.000000</td>\n",
       "      <td>479.000000</td>\n",
       "      <td>486.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>16.393004</td>\n",
       "      <td>26.993377</td>\n",
       "      <td>6.596708</td>\n",
       "      <td>13702.224280</td>\n",
       "      <td>47.914405</td>\n",
       "      <td>1987.485597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.367578</td>\n",
       "      <td>8.561462</td>\n",
       "      <td>3.327782</td>\n",
       "      <td>8692.118528</td>\n",
       "      <td>49.112574</td>\n",
       "      <td>6.340412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1963.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1979.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>7024.750000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>1982.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>16.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>11560.500000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>1986.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>24.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>19442.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>1991.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>31.000000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>35283.000000</td>\n",
       "      <td>251.000000</td>\n",
       "      <td>2002.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">M</th>\n",
       "      <th>count</th>\n",
       "      <td>485.000000</td>\n",
       "      <td>446.000000</td>\n",
       "      <td>485.000000</td>\n",
       "      <td>485.000000</td>\n",
       "      <td>479.000000</td>\n",
       "      <td>485.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>16.971134</td>\n",
       "      <td>25.786996</td>\n",
       "      <td>6.360825</td>\n",
       "      <td>15208.136082</td>\n",
       "      <td>39.321503</td>\n",
       "      <td>1988.641237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.431738</td>\n",
       "      <td>8.350303</td>\n",
       "      <td>3.452708</td>\n",
       "      <td>9395.610252</td>\n",
       "      <td>42.003947</td>\n",
       "      <td>6.825992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2063.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1979.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>6992.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>1982.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>12918.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>1987.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>24.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>22841.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>1995.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>31.000000</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>35479.000000</td>\n",
       "      <td>230.000000</td>\n",
       "      <td>2002.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>384 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           day  hindfoot_length        month     record_id  \\\n",
       "plot_id sex                                                                  \n",
       "1       F   count   848.000000       808.000000   848.000000    848.000000   \n",
       "            mean     15.338443        31.733911     6.597877  18390.384434   \n",
       "            std       8.325993         8.894939     3.366246  10469.790852   \n",
       "            min       1.000000        14.000000     1.000000      9.000000   \n",
       "            25%       9.000000        22.000000     4.000000   8783.500000   \n",
       "            50%      15.000000        34.000000     7.000000  19182.500000   \n",
       "            75%      22.000000        36.000000    10.000000  27691.750000   \n",
       "            max      31.000000        64.000000    12.000000  35393.000000   \n",
       "        M   count  1095.000000      1047.000000  1095.000000   1095.000000   \n",
       "            mean     15.905936        34.302770     6.121461  17197.740639   \n",
       "            std       8.053257         8.979955     3.418795   9911.570595   \n",
       "            min       1.000000        12.000000     1.000000      6.000000   \n",
       "            25%       9.000000        32.000000     3.000000   8638.000000   \n",
       "            50%      16.000000        36.000000     6.000000  17043.000000   \n",
       "            75%      23.000000        37.000000     9.000000  25251.500000   \n",
       "            max      31.000000        57.000000    12.000000  35390.000000   \n",
       "2       F   count   970.000000       918.000000   970.000000    970.000000   \n",
       "            mean     16.288660        30.161220     6.426804  17714.753608   \n",
       "            std       8.046509         8.677937     3.537694  10300.015076   \n",
       "            min       1.000000        14.000000     1.000000      3.000000   \n",
       "            25%      10.000000        21.000000     3.000000   9580.250000   \n",
       "            50%      16.000000        33.000000     6.000000  18104.500000   \n",
       "            75%      23.000000        36.000000    10.000000  26586.500000   \n",
       "            max      31.000000        57.000000    12.000000  35405.000000   \n",
       "        M   count  1144.000000      1077.000000  1144.000000   1144.000000   \n",
       "            mean     15.440559        30.353760     6.340035  18085.458042   \n",
       "            std       8.414667         9.016312     3.623430  10555.331260   \n",
       "            min       1.000000        13.000000     1.000000      1.000000   \n",
       "            25%       9.000000        21.000000     3.000000   8653.000000   \n",
       "            50%      15.000000        33.000000     6.000000  19024.500000   \n",
       "...                        ...              ...          ...           ...   \n",
       "23      F   std       8.776973         6.455268     3.353006   8854.378716   \n",
       "            min       1.000000        14.000000     1.000000     41.000000   \n",
       "            25%       9.000000        16.000000     4.000000   9536.000000   \n",
       "            50%      15.000000        18.000000     7.000000  14692.000000   \n",
       "            75%      24.000000        20.000000    10.000000  21169.000000   \n",
       "            max      30.000000        52.000000    12.000000  35489.000000   \n",
       "        M   count   207.000000       200.000000   207.000000    207.000000   \n",
       "            mean     16.077295        19.850000     6.391304  17091.338164   \n",
       "            std       7.955203         5.980496     3.543971   8852.413083   \n",
       "            min       1.000000        14.000000     1.000000     55.000000   \n",
       "            25%      10.000000        16.000000     3.000000  11274.000000   \n",
       "            50%      16.000000        18.000000     6.000000  15693.000000   \n",
       "            75%      23.000000        20.250000    10.000000  24740.500000   \n",
       "            max      31.000000        50.000000    12.000000  35282.000000   \n",
       "24      F   count   486.000000       453.000000   486.000000    486.000000   \n",
       "            mean     16.393004        26.993377     6.596708  13702.224280   \n",
       "            std       8.367578         8.561462     3.327782   8692.118528   \n",
       "            min       1.000000        12.000000     1.000000   1963.000000   \n",
       "            25%       9.000000        20.000000     4.000000   7024.750000   \n",
       "            50%      16.000000        22.000000     7.000000  11560.500000   \n",
       "            75%      24.000000        35.000000    10.000000  19442.000000   \n",
       "            max      31.000000        52.000000    12.000000  35283.000000   \n",
       "        M   count   485.000000       446.000000   485.000000    485.000000   \n",
       "            mean     16.971134        25.786996     6.360825  15208.136082   \n",
       "            std       8.431738         8.350303     3.452708   9395.610252   \n",
       "            min       1.000000        12.000000     1.000000   2063.000000   \n",
       "            25%      10.000000        19.000000     3.000000   6992.000000   \n",
       "            50%      17.000000        21.000000     6.000000  12918.000000   \n",
       "            75%      24.000000        35.000000    10.000000  22841.000000   \n",
       "            max      31.000000        51.000000    12.000000  35479.000000   \n",
       "\n",
       "                        weight         year  \n",
       "plot_id sex                                  \n",
       "1       F   count   826.000000   848.000000  \n",
       "            mean     46.311138  1990.933962  \n",
       "            std      33.240958     7.678171  \n",
       "            min       5.000000  1977.000000  \n",
       "            25%      26.000000  1983.000000  \n",
       "            50%      40.000000  1991.000000  \n",
       "            75%      50.000000  1998.000000  \n",
       "            max     196.000000  2002.000000  \n",
       "        M   count  1072.000000  1095.000000  \n",
       "            mean     55.950560  1990.091324  \n",
       "            std      41.035686     7.265208  \n",
       "            min       4.000000  1977.000000  \n",
       "            25%      37.000000  1983.000000  \n",
       "            50%      46.000000  1990.000000  \n",
       "            75%      54.000000  1997.000000  \n",
       "            max     231.000000  2002.000000  \n",
       "2       F   count   954.000000   970.000000  \n",
       "            mean     52.561845  1990.449485  \n",
       "            std      45.547697     7.519910  \n",
       "            min       5.000000  1977.000000  \n",
       "            25%      25.000000  1984.000000  \n",
       "            50%      40.000000  1990.000000  \n",
       "            75%      51.000000  1997.000000  \n",
       "            max     274.000000  2002.000000  \n",
       "        M   count  1114.000000  1144.000000  \n",
       "            mean     51.391382  1990.756119  \n",
       "            std      46.690887     7.714444  \n",
       "            min       5.000000  1977.000000  \n",
       "            25%      24.000000  1983.000000  \n",
       "            50%      42.000000  1991.000000  \n",
       "...                        ...          ...  \n",
       "23      F   std      18.933945     6.454297  \n",
       "            min       8.000000  1977.000000  \n",
       "            25%      12.000000  1984.000000  \n",
       "            50%      16.000000  1988.000000  \n",
       "            75%      23.000000  1993.000000  \n",
       "            max     199.000000  2002.000000  \n",
       "        M   count   205.000000   207.000000  \n",
       "            mean     18.941463  1989.961353  \n",
       "            std      17.979740     6.509027  \n",
       "            min       4.000000  1977.000000  \n",
       "            25%      10.000000  1986.000000  \n",
       "            50%      12.000000  1989.000000  \n",
       "            75%      22.000000  1996.000000  \n",
       "            max     131.000000  2002.000000  \n",
       "24      F   count   479.000000   486.000000  \n",
       "            mean     47.914405  1987.485597  \n",
       "            std      49.112574     6.340412  \n",
       "            min       6.000000  1979.000000  \n",
       "            25%      21.000000  1982.000000  \n",
       "            50%      33.000000  1986.000000  \n",
       "            75%      44.000000  1991.000000  \n",
       "            max     251.000000  2002.000000  \n",
       "        M   count   479.000000   485.000000  \n",
       "            mean     39.321503  1988.641237  \n",
       "            std      42.003947     6.825992  \n",
       "            min       4.000000  1979.000000  \n",
       "            25%      17.000000  1982.000000  \n",
       "            50%      24.000000  1987.000000  \n",
       "            75%      45.000000  1995.000000  \n",
       "            max     230.000000  2002.000000  \n",
       "\n",
       "[384 rows x 6 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "by_plot_sex.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "plot_sex_count = by_plot_sex['weight'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "plot_id  sex\n",
       "1        F      38253.0\n",
       "         M      59979.0\n",
       "2        F      50144.0\n",
       "         M      57250.0\n",
       "3        F      27251.0\n",
       "         M      28253.0\n",
       "4        F      39796.0\n",
       "         M      49377.0\n",
       "5        F      21143.0\n",
       "         M      23326.0\n",
       "6        F      26210.0\n",
       "         M      27245.0\n",
       "7        F       6522.0\n",
       "         M       6422.0\n",
       "8        F      37274.0\n",
       "         M      47755.0\n",
       "9        F      44128.0\n",
       "         M      48727.0\n",
       "10       F       2359.0\n",
       "         M       2776.0\n",
       "11       F      34638.0\n",
       "         M      43106.0\n",
       "12       F      51825.0\n",
       "         M      57420.0\n",
       "13       F      24720.0\n",
       "         M      30354.0\n",
       "14       F      32770.0\n",
       "         M      46469.0\n",
       "15       F      12455.0\n",
       "         M      11037.0\n",
       "16       F       5446.0\n",
       "         M       6310.0\n",
       "17       F      42106.0\n",
       "         M      48082.0\n",
       "18       F      27353.0\n",
       "         M      26433.0\n",
       "19       F      11297.0\n",
       "         M      11514.0\n",
       "20       F      33206.0\n",
       "         M      25988.0\n",
       "21       F      15481.0\n",
       "         M       9815.0\n",
       "22       F      34656.0\n",
       "         M      35363.0\n",
       "23       F       3352.0\n",
       "         M       3883.0\n",
       "24       F      22951.0\n",
       "         M      18835.0\n",
       "Name: weight, dtype: float64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_sex_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>sex</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>plot_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38253.0</td>\n",
       "      <td>59979.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>50144.0</td>\n",
       "      <td>57250.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>27251.0</td>\n",
       "      <td>28253.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>39796.0</td>\n",
       "      <td>49377.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>21143.0</td>\n",
       "      <td>23326.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>26210.0</td>\n",
       "      <td>27245.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>6522.0</td>\n",
       "      <td>6422.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>37274.0</td>\n",
       "      <td>47755.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>44128.0</td>\n",
       "      <td>48727.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2359.0</td>\n",
       "      <td>2776.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>34638.0</td>\n",
       "      <td>43106.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>51825.0</td>\n",
       "      <td>57420.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>24720.0</td>\n",
       "      <td>30354.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>32770.0</td>\n",
       "      <td>46469.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>12455.0</td>\n",
       "      <td>11037.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>5446.0</td>\n",
       "      <td>6310.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>42106.0</td>\n",
       "      <td>48082.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>27353.0</td>\n",
       "      <td>26433.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>11297.0</td>\n",
       "      <td>11514.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>33206.0</td>\n",
       "      <td>25988.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>15481.0</td>\n",
       "      <td>9815.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>34656.0</td>\n",
       "      <td>35363.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>3352.0</td>\n",
       "      <td>3883.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>22951.0</td>\n",
       "      <td>18835.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "sex            F        M\n",
       "plot_id                  \n",
       "1        38253.0  59979.0\n",
       "2        50144.0  57250.0\n",
       "3        27251.0  28253.0\n",
       "4        39796.0  49377.0\n",
       "5        21143.0  23326.0\n",
       "6        26210.0  27245.0\n",
       "7         6522.0   6422.0\n",
       "8        37274.0  47755.0\n",
       "9        44128.0  48727.0\n",
       "10        2359.0   2776.0\n",
       "11       34638.0  43106.0\n",
       "12       51825.0  57420.0\n",
       "13       24720.0  30354.0\n",
       "14       32770.0  46469.0\n",
       "15       12455.0  11037.0\n",
       "16        5446.0   6310.0\n",
       "17       42106.0  48082.0\n",
       "18       27353.0  26433.0\n",
       "19       11297.0  11514.0\n",
       "20       33206.0  25988.0\n",
       "21       15481.0   9815.0\n",
       "22       34656.0  35363.0\n",
       "23        3352.0   3883.0\n",
       "24       22951.0  18835.0"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_sex_count.unstack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "spc = plot_sex_count.unstack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x119a20f28>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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jzm0yM+uUOuz3bCJim6QvAb8GugLXR8TSFh5uuuMc5zjHtZG62lPc2zrsBAEz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119cfac88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "s_plot = spc.plot(kind='bar',stacked=True,title=\"Total weight by plot and sex\")\n",
    "s_plot.set_ylabel(\"Weight\")\n",
    "s_plot.set_xlabel(\"Plot\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         NL\n",
       "1         NL\n",
       "2         DM\n",
       "3         DM\n",
       "4         DM\n",
       "5         PF\n",
       "6         PE\n",
       "7         DM\n",
       "8         DM\n",
       "9         PF\n",
       "10        DS\n",
       "11        DM\n",
       "12        DM\n",
       "13        DM\n",
       "14        DM\n",
       "15        DM\n",
       "16        DS\n",
       "17        PP\n",
       "18        PF\n",
       "19        DS\n",
       "20        DM\n",
       "21        NL\n",
       "22        DM\n",
       "23        SH\n",
       "24        DM\n",
       "25        DM\n",
       "26        DM\n",
       "27        DM\n",
       "28        PP\n",
       "29        DS\n",
       "        ... \n",
       "35519     SF\n",
       "35520     DM\n",
       "35521     DM\n",
       "35522     DM\n",
       "35523     PB\n",
       "35524     OL\n",
       "35525     OT\n",
       "35526     DO\n",
       "35527     US\n",
       "35528     PB\n",
       "35529     OT\n",
       "35530     PB\n",
       "35531     DM\n",
       "35532     DM\n",
       "35533     DM\n",
       "35534     DM\n",
       "35535     DM\n",
       "35536     DM\n",
       "35537     PB\n",
       "35538     SF\n",
       "35539     PB\n",
       "35540     PB\n",
       "35541     PB\n",
       "35542     PB\n",
       "35543     US\n",
       "35544     AH\n",
       "35545     AH\n",
       "35546     RM\n",
       "35547     DO\n",
       "35548    NaN\n",
       "Name: species_id, dtype: object"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "surveys_df['species_id']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         NL\n",
       "1         NL\n",
       "2         DM\n",
       "3         DM\n",
       "4         DM\n",
       "5         PF\n",
       "6         PE\n",
       "7         DM\n",
       "8         DM\n",
       "9         PF\n",
       "10        DS\n",
       "11        DM\n",
       "12        DM\n",
       "13        DM\n",
       "14        DM\n",
       "15        DM\n",
       "16        DS\n",
       "17        PP\n",
       "18        PF\n",
       "19        DS\n",
       "20        DM\n",
       "21        NL\n",
       "22        DM\n",
       "23        SH\n",
       "24        DM\n",
       "25        DM\n",
       "26        DM\n",
       "27        DM\n",
       "28        PP\n",
       "29        DS\n",
       "        ... \n",
       "35519     SF\n",
       "35520     DM\n",
       "35521     DM\n",
       "35522     DM\n",
       "35523     PB\n",
       "35524     OL\n",
       "35525     OT\n",
       "35526     DO\n",
       "35527     US\n",
       "35528     PB\n",
       "35529     OT\n",
       "35530     PB\n",
       "35531     DM\n",
       "35532     DM\n",
       "35533     DM\n",
       "35534     DM\n",
       "35535     DM\n",
       "35536     DM\n",
       "35537     PB\n",
       "35538     SF\n",
       "35539     PB\n",
       "35540     PB\n",
       "35541     PB\n",
       "35542     PB\n",
       "35543     US\n",
       "35544     AH\n",
       "35545     AH\n",
       "35546     RM\n",
       "35547     DO\n",
       "35548    NaN\n",
       "Name: species_id, dtype: object"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "surveys_df.species_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>species_id</th>\n",
       "      <th>sex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NL</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NL</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DM</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DM</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DM</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>PF</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>PE</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>DM</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>DM</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>PF</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>DS</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>DM</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>DM</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>DM</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>DM</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>DM</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>DS</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>PP</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>PF</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>DS</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>DM</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>NL</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>DM</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>SH</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>DM</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>DM</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>DM</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>DM</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>PP</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>DS</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35519</th>\n",
       "      <td>SF</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35520</th>\n",
       "      <td>DM</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35521</th>\n",
       "      <td>DM</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35522</th>\n",
       "      <td>DM</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35523</th>\n",
       "      <td>PB</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35524</th>\n",
       "      <td>OL</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35525</th>\n",
       "      <td>OT</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35526</th>\n",
       "      <td>DO</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35527</th>\n",
       "      <td>US</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35528</th>\n",
       "      <td>PB</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35529</th>\n",
       "      <td>OT</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35530</th>\n",
       "      <td>PB</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35531</th>\n",
       "      <td>DM</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35532</th>\n",
       "      <td>DM</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35533</th>\n",
       "      <td>DM</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35534</th>\n",
       "      <td>DM</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35535</th>\n",
       "      <td>DM</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35536</th>\n",
       "      <td>DM</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35537</th>\n",
       "      <td>PB</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35538</th>\n",
       "      <td>SF</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35539</th>\n",
       "      <td>PB</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35540</th>\n",
       "      <td>PB</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35541</th>\n",
       "      <td>PB</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35542</th>\n",
       "      <td>PB</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35543</th>\n",
       "      <td>US</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35544</th>\n",
       "      <td>AH</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35545</th>\n",
       "      <td>AH</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35546</th>\n",
       "      <td>RM</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35547</th>\n",
       "      <td>DO</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35548</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>35549 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      species_id  sex\n",
       "0             NL    M\n",
       "1             NL    M\n",
       "2             DM    F\n",
       "3             DM    M\n",
       "4             DM    M\n",
       "5             PF    M\n",
       "6             PE    F\n",
       "7             DM    M\n",
       "8             DM    F\n",
       "9             PF    F\n",
       "10            DS    F\n",
       "11            DM    M\n",
       "12            DM    M\n",
       "13            DM  NaN\n",
       "14            DM    F\n",
       "15            DM    F\n",
       "16            DS    F\n",
       "17            PP    M\n",
       "18            PF  NaN\n",
       "19            DS    F\n",
       "20            DM    F\n",
       "21            NL    F\n",
       "22            DM    M\n",
       "23            SH    M\n",
       "24            DM    M\n",
       "25            DM    M\n",
       "26            DM    M\n",
       "27            DM    M\n",
       "28            PP    M\n",
       "29            DS    F\n",
       "...          ...  ...\n",
       "35519         SF  NaN\n",
       "35520         DM    M\n",
       "35521         DM    F\n",
       "35522         DM    F\n",
       "35523         PB    F\n",
       "35524         OL    M\n",
       "35525         OT    F\n",
       "35526         DO    F\n",
       "35527         US  NaN\n",
       "35528         PB    F\n",
       "35529         OT    F\n",
       "35530         PB    F\n",
       "35531         DM    F\n",
       "35532         DM    F\n",
       "35533         DM    M\n",
       "35534         DM    M\n",
       "35535         DM    F\n",
       "35536         DM    F\n",
       "35537         PB    F\n",
       "35538         SF    M\n",
       "35539         PB    F\n",
       "35540         PB    F\n",
       "35541         PB    F\n",
       "35542         PB    F\n",
       "35543         US  NaN\n",
       "35544         AH  NaN\n",
       "35545         AH  NaN\n",
       "35546         RM    F\n",
       "35547         DO    M\n",
       "35548        NaN  NaN\n",
       "\n",
       "[35549 rows x 2 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "surveys_df[['species_id','sex']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "surveys_species = surveys_df['species_id']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         NL\n",
       "1         NL\n",
       "2         DM\n",
       "3         DM\n",
       "4         DM\n",
       "5         PF\n",
       "6         PE\n",
       "7         DM\n",
       "8         DM\n",
       "9         PF\n",
       "10        DS\n",
       "11        DM\n",
       "12        DM\n",
       "13        DM\n",
       "14        DM\n",
       "15        DM\n",
       "16        DS\n",
       "17        PP\n",
       "18        PF\n",
       "19        DS\n",
       "20        DM\n",
       "21        NL\n",
       "22        DM\n",
       "23        SH\n",
       "24        DM\n",
       "25        DM\n",
       "26        DM\n",
       "27        DM\n",
       "28        PP\n",
       "29        DS\n",
       "        ... \n",
       "35519     SF\n",
       "35520     DM\n",
       "35521     DM\n",
       "35522     DM\n",
       "35523     PB\n",
       "35524     OL\n",
       "35525     OT\n",
       "35526     DO\n",
       "35527     US\n",
       "35528     PB\n",
       "35529     OT\n",
       "35530     PB\n",
       "35531     DM\n",
       "35532     DM\n",
       "35533     DM\n",
       "35534     DM\n",
       "35535     DM\n",
       "35536     DM\n",
       "35537     PB\n",
       "35538     SF\n",
       "35539     PB\n",
       "35540     PB\n",
       "35541     PB\n",
       "35542     PB\n",
       "35543     US\n",
       "35544     AH\n",
       "35545     AH\n",
       "35546     RM\n",
       "35547     DO\n",
       "35548    NaN\n",
       "Name: species_id, dtype: object"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "surveys_species"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>plot_id</th>\n",
       "      <th>species_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>NL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>NL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>DM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7</td>\n",
       "      <td>DM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>DM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>PF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2</td>\n",
       "      <td>PE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>DM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>DM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>6</td>\n",
       "      <td>PF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5</td>\n",
       "      <td>DS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>7</td>\n",
       "      <td>DM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>3</td>\n",
       "      <td>DM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>8</td>\n",
       "      <td>DM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>6</td>\n",
       "      <td>DM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>4</td>\n",
       "      <td>DM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>3</td>\n",
       "      <td>DS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2</td>\n",
       "      <td>PP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>4</td>\n",
       "      <td>PF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>11</td>\n",
       "      <td>DS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>14</td>\n",
       "      <td>DM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>15</td>\n",
       "      <td>NL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>13</td>\n",
       "      <td>DM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>13</td>\n",
       "      <td>SH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>9</td>\n",
       "      <td>DM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>15</td>\n",
       "      <td>DM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>15</td>\n",
       "      <td>DM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>11</td>\n",
       "      <td>DM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>11</td>\n",
       "      <td>PP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>10</td>\n",
       "      <td>DS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35519</th>\n",
       "      <td>9</td>\n",
       "      <td>SF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35520</th>\n",
       "      <td>9</td>\n",
       "      <td>DM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35521</th>\n",
       "      <td>9</td>\n",
       "      <td>DM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35522</th>\n",
       "      <td>9</td>\n",
       "      <td>DM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35523</th>\n",
       "      <td>9</td>\n",
       "      <td>PB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35524</th>\n",
       "      <td>9</td>\n",
       "      <td>OL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35525</th>\n",
       "      <td>8</td>\n",
       "      <td>OT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35526</th>\n",
       "      <td>13</td>\n",
       "      <td>DO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35527</th>\n",
       "      <td>13</td>\n",
       "      <td>US</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35528</th>\n",
       "      <td>13</td>\n",
       "      <td>PB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35529</th>\n",
       "      <td>13</td>\n",
       "      <td>OT</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "       plot_id species_id\n",
       "0            2         NL\n",
       "1            3         NL\n",
       "2            2         DM\n",
       "3            7         DM\n",
       "4            3         DM\n",
       "5            1         PF\n",
       "6            2         PE\n",
       "7            1         DM\n",
       "8            1         DM\n",
       "9            6         PF\n",
       "10           5         DS\n",
       "11           7         DM\n",
       "12           3         DM\n",
       "13           8         DM\n",
       "14           6         DM\n",
       "15           4         DM\n",
       "16           3         DS\n",
       "17           2         PP\n",
       "18           4         PF\n",
       "19          11         DS\n",
       "20          14         DM\n",
       "21          15         NL\n",
       "22          13         DM\n",
       "23          13         SH\n",
       "24           9         DM\n",
       "25          15         DM\n",
       "26          15         DM\n",
       "27          11         DM\n",
       "28          11         PP\n",
       "29          10         DS\n",
       "...        ...        ...\n",
       "35519        9         SF\n",
       "35520        9         DM\n",
       "35521        9         DM\n",
       "35522        9         DM\n",
       "35523        9         PB\n",
       "35524        9         OL\n",
       "35525        8         OT\n",
       "35526       13         DO\n",
       "35527       13         US\n",
       "35528       13         PB\n",
       "35529       13         OT\n",
       "35530       13         PB\n",
       "35531       14         DM\n",
       "35532       14         DM\n",
       "35533       14         DM\n",
       "35534       14         DM\n",
       "35535       14         DM\n",
       "35536       14         DM\n",
       "35537       15         PB\n",
       "35538       15         SF\n",
       "35539       15         PB\n",
       "35540       15         PB\n",
       "35541       15         PB\n",
       "35542       15         PB\n",
       "35543       15         US\n",
       "35544       15         AH\n",
       "35545       15         AH\n",
       "35546       10         RM\n",
       "35547        7         DO\n",
       "35548        5        NaN\n",
       "\n",
       "[35549 rows x 2 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "surveys_df[['plot_id', 'species_id']]"
   ]
  },
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   "execution_count": 54,
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       "   record_id  month  day  year  plot_id species_id sex  hindfoot_length  \\\n",
       "0          1      7   16  1977        2         NL   M             32.0   \n",
       "1          2      7   16  1977        3         NL   M             33.0   \n",
       "2          3      7   16  1977        2         DM   F             37.0   \n",
       "\n",
       "   weight  \n",
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     "metadata": {},
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    "surveys_df[0:3]"
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  {
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   "execution_count": 56,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
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       "  species_id sex\n",
       "0         NL   M\n",
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     "execution_count": 56,
     "metadata": {},
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   "source": [
    "surveys_df[0:3][['species_id','sex']]"
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  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": [
    "a = list(range(10))"
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  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]"
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     "execution_count": 58,
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     "output_type": "execute_result"
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    "a"
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   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": [
    "sl = list(surveys_df.sex)"
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  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
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   "outputs": [
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
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       "  </thead>\n",
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       "      <th>45</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
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       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>61</td>\n",
       "      <td>7</td>\n",
       "      <td>18</td>\n",
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       "      <td>DM</td>\n",
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       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "    record_id  month  day  year  plot_id species_id sex  hindfoot_length  \\\n",
       "20         21      7   17  1977       14         DM   F             34.0   \n",
       "25         26      7   17  1977       15         DM   M             31.0   \n",
       "30         31      7   17  1977       15         DM   F             37.0   \n",
       "35         36      7   17  1977       16         OT   F             22.0   \n",
       "40         41      7   18  1977       23         DM   F             34.0   \n",
       "45         46      7   18  1977       19         DM   M             35.0   \n",
       "50         51      7   18  1977       21         DM   F             36.0   \n",
       "55         56      7   18  1977       20         DM   M             34.0   \n",
       "60         61      7   18  1977       23         DM   M             35.0   \n",
       "\n",
       "    weight  \n",
       "20     NaN  \n",
       "25     NaN  \n",
       "30     NaN  \n",
       "35     NaN  \n",
       "40     NaN  \n",
       "45     NaN  \n",
       "50     NaN  \n",
       "55     NaN  \n",
       "60     NaN  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "surveys_df[20:61:5]"
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  },
  {
   "cell_type": "code",
   "execution_count": 73,
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   "outputs": [],
   "source": [
    "s = surveys_df[['plot_id','sex']]"
   ]
  },
  {
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       "<p>35549 rows × 2 columns</p>\n",
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      ],
      "text/plain": [
       "       plot_id  sex\n",
       "0            2    M\n",
       "1            3    M\n",
       "2            2    F\n",
       "3            7    M\n",
       "4            3    M\n",
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       "13           8  NaN\n",
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       "15           4    F\n",
       "16           3    F\n",
       "17           2    M\n",
       "18           4  NaN\n",
       "19          11    F\n",
       "20          14    F\n",
       "21          15    F\n",
       "22          13    M\n",
       "23          13    M\n",
       "24           9    M\n",
       "25          15    M\n",
       "26          15    M\n",
       "27          11    M\n",
       "28          11    M\n",
       "29          10    F\n",
       "...        ...  ...\n",
       "35519        9  NaN\n",
       "35520        9    M\n",
       "35521        9    F\n",
       "35522        9    F\n",
       "35523        9    F\n",
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       "35525        8    F\n",
       "35526       13    F\n",
       "35527       13  NaN\n",
       "35528       13    F\n",
       "35529       13    F\n",
       "35530       13    F\n",
       "35531       14    F\n",
       "35532       14    F\n",
       "35533       14    M\n",
       "35534       14    M\n",
       "35535       14    F\n",
       "35536       14    F\n",
       "35537       15    F\n",
       "35538       15    M\n",
       "35539       15    F\n",
       "35540       15    F\n",
       "35541       15    F\n",
       "35542       15    F\n",
       "35543       15  NaN\n",
       "35544       15  NaN\n",
       "35545       15  NaN\n",
       "35546       10    F\n",
       "35547        7    M\n",
       "35548        5  NaN\n",
       "\n",
       "[35549 rows x 2 columns]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s"
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  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "surveys_copy = surveys_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     33038\n",
       "unique        2\n",
       "top           M\n",
       "freq      17348\n",
       "Name: sex, dtype: object"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "surveys_copy.sex.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "surveys_copy[0:3] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "   record_id  month  day  year  plot_id species_id sex  hindfoot_length  \\\n",
       "0          0      0    0     0        0          0   0              0.0   \n",
       "1          0      0    0     0        0          0   0              0.0   \n",
       "2          0      0    0     0        0          0   0              0.0   \n",
       "3          4      7   16  1977        7         DM   M             36.0   \n",
       "4          5      7   16  1977        3         DM   M             35.0   \n",
       "\n",
       "   weight  \n",
       "0     0.0  \n",
       "1     0.0  \n",
       "2     0.0  \n",
       "3     NaN  \n",
       "4     NaN  "
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "surveys_copy.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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      "text/plain": [
       "   record_id  month  day  year  plot_id species_id sex  hindfoot_length  \\\n",
       "0          0      0    0     0        0          0   0              0.0   \n",
       "1          0      0    0     0        0          0   0              0.0   \n",
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       "4          5      7   16  1977        3         DM   M             35.0   \n",
       "5          6      7   16  1977        1         PF   M             14.0   \n",
       "\n",
       "   weight  \n",
       "0     0.0  \n",
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       "2     0.0  \n",
       "3     NaN  \n",
       "4     NaN  \n",
       "5     NaN  "
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "surveys_df.head(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "a = 5\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "a"
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  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "b = a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "b"
   ]
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  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "b = 6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "a"
   ]
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  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "surveys_df = pd.read_csv(\"surveys.csv\")\n",
    "surveys_copy= surveys_df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "surveys_copy[0:3] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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       "5     NaN  "
      ]
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     "execution_count": 90,
     "metadata": {},
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    "surveys_copy.head(6)"
   ]
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  {
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   "metadata": {
    "collapsed": false
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       "1          2      7   16  1977        3         NL   M             33.0   \n",
       "2          3      7   16  1977        2         DM   F             37.0   \n",
       "3          4      7   16  1977        7         DM   M             36.0   \n",
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       "5          6      7   16  1977        1         PF   M             14.0   \n",
       "\n",
       "   weight  \n",
       "0     NaN  \n",
       "1     NaN  \n",
       "2     NaN  \n",
       "3     NaN  \n",
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      ]
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     "execution_count": 91,
     "metadata": {},
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    "surveys_df.head(6)"
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    "collapsed": false
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       "   month  day  year\n",
       "0      7   16  1977\n",
       "1      7   16  1977\n",
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    "surveys_df.iloc[0:3, 1:4]"
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   "execution_count": 96,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'PF'"
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     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
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    "surveys_df.loc[5, 'species_id']"
   ]
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   "metadata": {
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    {
     "data": {
      "text/plain": [
       "'PF'"
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     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
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    "surveys_df.iloc[5, 5]"
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  {
   "cell_type": "code",
   "execution_count": 99,
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       "    record_id  month  day  year  plot_id species_id sex  hindfoot_length  \\\n",
       "0           1      7   16  1977        2         NL   M             32.0   \n",
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     "metadata": {},
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    "surveys_df.loc[[0, 10], :]"
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   "execution_count": 100,
   "metadata": {
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    {
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       "species_id    DM\n",
       "plot_id        8\n",
       "weight        36\n",
       "Name: 777, dtype: object"
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     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "surveys_df.loc[777, ['species_id', 'plot_id', 'weight']]"
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   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
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    "surveys_df.iloc[1:4, :2]"
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     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "surveys_df.loc[[1,3,5],['species_id','sex']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "  species_id sex\n",
       "1         NL   M\n",
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     "execution_count": 104,
     "metadata": {},
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    "surveys_df.iloc[1:6:2,[5,6]]"
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  {
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       "      <td>NL</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>33340</td>\n",
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       "      <td>33342</td>\n",
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       "      <td>12</td>\n",
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       "      <td>26.0</td>\n",
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       "    <tr>\n",
       "      <th>33342</th>\n",
       "      <td>33343</td>\n",
       "      <td>1</td>\n",
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       "      <td>12</td>\n",
       "      <td>DO</td>\n",
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       "      <td>47.0</td>\n",
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       "    <tr>\n",
       "      <th>33343</th>\n",
       "      <td>33344</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
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       "      <td>12</td>\n",
       "      <td>DM</td>\n",
       "      <td>M</td>\n",
       "      <td>36.0</td>\n",
       "      <td>40.0</td>\n",
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       "    <tr>\n",
       "      <th>33344</th>\n",
       "      <td>33345</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>2002</td>\n",
       "      <td>12</td>\n",
       "      <td>DO</td>\n",
       "      <td>M</td>\n",
       "      <td>37.0</td>\n",
       "      <td>55.0</td>\n",
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       "    <tr>\n",
       "      <th>33345</th>\n",
       "      <td>33346</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>2002</td>\n",
       "      <td>12</td>\n",
       "      <td>PE</td>\n",
       "      <td>M</td>\n",
       "      <td>21.0</td>\n",
       "      <td>23.0</td>\n",
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       "    <tr>\n",
       "      <th>33346</th>\n",
       "      <td>33347</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>2002</td>\n",
       "      <td>12</td>\n",
       "      <td>DM</td>\n",
       "      <td>F</td>\n",
       "      <td>37.0</td>\n",
       "      <td>45.0</td>\n",
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       "    <tr>\n",
       "      <th>33347</th>\n",
       "      <td>33348</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
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       "      <td>19</td>\n",
       "      <td>PB</td>\n",
       "      <td>M</td>\n",
       "      <td>29.0</td>\n",
       "      <td>51.0</td>\n",
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       "    <tr>\n",
       "      <th>33348</th>\n",
       "      <td>33349</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>2002</td>\n",
       "      <td>19</td>\n",
       "      <td>PB</td>\n",
       "      <td>M</td>\n",
       "      <td>27.0</td>\n",
       "      <td>46.0</td>\n",
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       "    <tr>\n",
       "      <th>33349</th>\n",
       "      <td>33350</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
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       "      <td>F</td>\n",
       "      <td>20.0</td>\n",
       "      <td>13.0</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <th>35519</th>\n",
       "      <td>35520</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>2002</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>24.0</td>\n",
       "      <td>36.0</td>\n",
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       "    <tr>\n",
       "      <th>35520</th>\n",
       "      <td>35521</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>2002</td>\n",
       "      <td>9</td>\n",
       "      <td>DM</td>\n",
       "      <td>M</td>\n",
       "      <td>37.0</td>\n",
       "      <td>48.0</td>\n",
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       "    <tr>\n",
       "      <th>35521</th>\n",
       "      <td>35522</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>2002</td>\n",
       "      <td>9</td>\n",
       "      <td>DM</td>\n",
       "      <td>F</td>\n",
       "      <td>35.0</td>\n",
       "      <td>45.0</td>\n",
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       "    <tr>\n",
       "      <th>35522</th>\n",
       "      <td>35523</td>\n",
       "      <td>12</td>\n",
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       "      <td>9</td>\n",
       "      <td>DM</td>\n",
       "      <td>F</td>\n",
       "      <td>36.0</td>\n",
       "      <td>44.0</td>\n",
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       "    <tr>\n",
       "      <th>35523</th>\n",
       "      <td>35524</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>2002</td>\n",
       "      <td>9</td>\n",
       "      <td>PB</td>\n",
       "      <td>F</td>\n",
       "      <td>25.0</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35524</th>\n",
       "      <td>35525</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>2002</td>\n",
       "      <td>9</td>\n",
       "      <td>OL</td>\n",
       "      <td>M</td>\n",
       "      <td>21.0</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35525</th>\n",
       "      <td>35526</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>2002</td>\n",
       "      <td>8</td>\n",
       "      <td>OT</td>\n",
       "      <td>F</td>\n",
       "      <td>20.0</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35526</th>\n",
       "      <td>35527</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>2002</td>\n",
       "      <td>13</td>\n",
       "      <td>DO</td>\n",
       "      <td>F</td>\n",
       "      <td>33.0</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35527</th>\n",
       "      <td>35528</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>2002</td>\n",
       "      <td>13</td>\n",
       "      <td>US</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35528</th>\n",
       "      <td>35529</td>\n",
       "      <td>12</td>\n",
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       "      <td>13</td>\n",
       "      <td>PB</td>\n",
       "      <td>F</td>\n",
       "      <td>25.0</td>\n",
       "      <td>25.0</td>\n",
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       "    <tr>\n",
       "      <th>35529</th>\n",
       "      <td>35530</td>\n",
       "      <td>12</td>\n",
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       "      <td>2002</td>\n",
       "      <td>13</td>\n",
       "      <td>OT</td>\n",
       "      <td>F</td>\n",
       "      <td>20.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35530</th>\n",
       "      <td>35531</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>2002</td>\n",
       "      <td>13</td>\n",
       "      <td>PB</td>\n",
       "      <td>F</td>\n",
       "      <td>27.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35531</th>\n",
       "      <td>35532</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>2002</td>\n",
       "      <td>14</td>\n",
       "      <td>DM</td>\n",
       "      <td>F</td>\n",
       "      <td>34.0</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35532</th>\n",
       "      <td>35533</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>2002</td>\n",
       "      <td>14</td>\n",
       "      <td>DM</td>\n",
       "      <td>F</td>\n",
       "      <td>36.0</td>\n",
       "      <td>48.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35533</th>\n",
       "      <td>35534</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>2002</td>\n",
       "      <td>14</td>\n",
       "      <td>DM</td>\n",
       "      <td>M</td>\n",
       "      <td>37.0</td>\n",
       "      <td>56.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35534</th>\n",
       "      <td>35535</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>2002</td>\n",
       "      <td>14</td>\n",
       "      <td>DM</td>\n",
       "      <td>M</td>\n",
       "      <td>37.0</td>\n",
       "      <td>53.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35535</th>\n",
       "      <td>35536</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>2002</td>\n",
       "      <td>14</td>\n",
       "      <td>DM</td>\n",
       "      <td>F</td>\n",
       "      <td>35.0</td>\n",
       "      <td>42.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35536</th>\n",
       "      <td>35537</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>2002</td>\n",
       "      <td>14</td>\n",
       "      <td>DM</td>\n",
       "      <td>F</td>\n",
       "      <td>36.0</td>\n",
       "      <td>46.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35537</th>\n",
       "      <td>35538</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>2002</td>\n",
       "      <td>15</td>\n",
       "      <td>PB</td>\n",
       "      <td>F</td>\n",
       "      <td>26.0</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35538</th>\n",
       "      <td>35539</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>2002</td>\n",
       "      <td>15</td>\n",
       "      <td>SF</td>\n",
       "      <td>M</td>\n",
       "      <td>26.0</td>\n",
       "      <td>68.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35539</th>\n",
       "      <td>35540</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>2002</td>\n",
       "      <td>15</td>\n",
       "      <td>PB</td>\n",
       "      <td>F</td>\n",
       "      <td>26.0</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35540</th>\n",
       "      <td>35541</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>2002</td>\n",
       "      <td>15</td>\n",
       "      <td>PB</td>\n",
       "      <td>F</td>\n",
       "      <td>24.0</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35541</th>\n",
       "      <td>35542</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>2002</td>\n",
       "      <td>15</td>\n",
       "      <td>PB</td>\n",
       "      <td>F</td>\n",
       "      <td>26.0</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35542</th>\n",
       "      <td>35543</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>2002</td>\n",
       "      <td>15</td>\n",
       "      <td>PB</td>\n",
       "      <td>F</td>\n",
       "      <td>27.0</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35543</th>\n",
       "      <td>35544</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>2002</td>\n",
       "      <td>15</td>\n",
       "      <td>US</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35544</th>\n",
       "      <td>35545</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>2002</td>\n",
       "      <td>15</td>\n",
       "      <td>AH</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35545</th>\n",
       "      <td>35546</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>2002</td>\n",
       "      <td>15</td>\n",
       "      <td>AH</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35546</th>\n",
       "      <td>35547</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>2002</td>\n",
       "      <td>10</td>\n",
       "      <td>RM</td>\n",
       "      <td>F</td>\n",
       "      <td>15.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35547</th>\n",
       "      <td>35548</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>2002</td>\n",
       "      <td>7</td>\n",
       "      <td>DO</td>\n",
       "      <td>M</td>\n",
       "      <td>36.0</td>\n",
       "      <td>51.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35548</th>\n",
       "      <td>35549</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>2002</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2229 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       record_id  month  day  year  plot_id species_id  sex  hindfoot_length  \\\n",
       "33320      33321      1   12  2002        1         DM    M             38.0   \n",
       "33321      33322      1   12  2002        1         DO    M             37.0   \n",
       "33322      33323      1   12  2002        1         PB    M             28.0   \n",
       "33323      33324      1   12  2002        1         AB  NaN              NaN   \n",
       "33324      33325      1   12  2002        1         DO    M             35.0   \n",
       "33325      33326      1   12  2002        2         OT    F             20.0   \n",
       "33326      33327      1   12  2002        2         OT    M             20.0   \n",
       "33327      33328      1   12  2002        2         OT    F             21.0   \n",
       "33328      33329      1   12  2002        2         DM    M             37.0   \n",
       "33329      33330      1   12  2002        2         DO    M             35.0   \n",
       "33330      33331      1   12  2002        2         PE    F             21.0   \n",
       "33331      33332      1   12  2002        2         OT    F             20.0   \n",
       "33332      33333      1   12  2002        2         OT    M             20.0   \n",
       "33333      33334      1   12  2002        2         OT    F             20.0   \n",
       "33334      33335      1   12  2002        2         DO    F             36.0   \n",
       "33335      33336      1   12  2002        2         DM    F             35.0   \n",
       "33336      33337      1   12  2002        2         PB    M             28.0   \n",
       "33337      33338      1   12  2002        2         PB    F             26.0   \n",
       "33338      33339      1   12  2002        2         NL  NaN              NaN   \n",
       "33339      33340      1   12  2002       12         DO    M             34.0   \n",
       "33340      33341      1   12  2002       12         PE    F             20.0   \n",
       "33341      33342      1   12  2002       12         DO    F             36.0   \n",
       "33342      33343      1   12  2002       12         DO    F             37.0   \n",
       "33343      33344      1   12  2002       12         DM    M             36.0   \n",
       "33344      33345      1   12  2002       12         DO    M             37.0   \n",
       "33345      33346      1   12  2002       12         PE    M             21.0   \n",
       "33346      33347      1   12  2002       12         DM    F             37.0   \n",
       "33347      33348      1   12  2002       19         PB    M             29.0   \n",
       "33348      33349      1   12  2002       19         PB    M             27.0   \n",
       "33349      33350      1   12  2002       19         PP    F             20.0   \n",
       "...          ...    ...  ...   ...      ...        ...  ...              ...   \n",
       "35519      35520     12   31  2002        9         SF  NaN             24.0   \n",
       "35520      35521     12   31  2002        9         DM    M             37.0   \n",
       "35521      35522     12   31  2002        9         DM    F             35.0   \n",
       "35522      35523     12   31  2002        9         DM    F             36.0   \n",
       "35523      35524     12   31  2002        9         PB    F             25.0   \n",
       "35524      35525     12   31  2002        9         OL    M             21.0   \n",
       "35525      35526     12   31  2002        8         OT    F             20.0   \n",
       "35526      35527     12   31  2002       13         DO    F             33.0   \n",
       "35527      35528     12   31  2002       13         US  NaN              NaN   \n",
       "35528      35529     12   31  2002       13         PB    F             25.0   \n",
       "35529      35530     12   31  2002       13         OT    F             20.0   \n",
       "35530      35531     12   31  2002       13         PB    F             27.0   \n",
       "35531      35532     12   31  2002       14         DM    F             34.0   \n",
       "35532      35533     12   31  2002       14         DM    F             36.0   \n",
       "35533      35534     12   31  2002       14         DM    M             37.0   \n",
       "35534      35535     12   31  2002       14         DM    M             37.0   \n",
       "35535      35536     12   31  2002       14         DM    F             35.0   \n",
       "35536      35537     12   31  2002       14         DM    F             36.0   \n",
       "35537      35538     12   31  2002       15         PB    F             26.0   \n",
       "35538      35539     12   31  2002       15         SF    M             26.0   \n",
       "35539      35540     12   31  2002       15         PB    F             26.0   \n",
       "35540      35541     12   31  2002       15         PB    F             24.0   \n",
       "35541      35542     12   31  2002       15         PB    F             26.0   \n",
       "35542      35543     12   31  2002       15         PB    F             27.0   \n",
       "35543      35544     12   31  2002       15         US  NaN              NaN   \n",
       "35544      35545     12   31  2002       15         AH  NaN              NaN   \n",
       "35545      35546     12   31  2002       15         AH  NaN              NaN   \n",
       "35546      35547     12   31  2002       10         RM    F             15.0   \n",
       "35547      35548     12   31  2002        7         DO    M             36.0   \n",
       "35548      35549     12   31  2002        5        NaN  NaN              NaN   \n",
       "\n",
       "       weight  \n",
       "33320    44.0  \n",
       "33321    58.0  \n",
       "33322    45.0  \n",
       "33323     NaN  \n",
       "33324    29.0  \n",
       "33325    26.0  \n",
       "33326    24.0  \n",
       "33327    22.0  \n",
       "33328    47.0  \n",
       "33329    51.0  \n",
       "33330    23.0  \n",
       "33331    18.0  \n",
       "33332    25.0  \n",
       "33333    22.0  \n",
       "33334    46.0  \n",
       "33335    45.0  \n",
       "33336    47.0  \n",
       "33337    30.0  \n",
       "33338     NaN  \n",
       "33339    24.0  \n",
       "33340    15.0  \n",
       "33341    26.0  \n",
       "33342    47.0  \n",
       "33343    40.0  \n",
       "33344    55.0  \n",
       "33345    23.0  \n",
       "33346    45.0  \n",
       "33347    51.0  \n",
       "33348    46.0  \n",
       "33349    13.0  \n",
       "...       ...  \n",
       "35519    36.0  \n",
       "35520    48.0  \n",
       "35521    45.0  \n",
       "35522    44.0  \n",
       "35523    27.0  \n",
       "35524    26.0  \n",
       "35525    24.0  \n",
       "35526    43.0  \n",
       "35527     NaN  \n",
       "35528    25.0  \n",
       "35529     NaN  \n",
       "35530     NaN  \n",
       "35531    43.0  \n",
       "35532    48.0  \n",
       "35533    56.0  \n",
       "35534    53.0  \n",
       "35535    42.0  \n",
       "35536    46.0  \n",
       "35537    31.0  \n",
       "35538    68.0  \n",
       "35539    23.0  \n",
       "35540    31.0  \n",
       "35541    29.0  \n",
       "35542    34.0  \n",
       "35543     NaN  \n",
       "35544     NaN  \n",
       "35545     NaN  \n",
       "35546    14.0  \n",
       "35547    51.0  \n",
       "35548     NaN  \n",
       "\n",
       "[2229 rows x 9 columns]"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "surveys_df[surveys_df.year == 2002]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        False\n",
       "1        False\n",
       "2        False\n",
       "3        False\n",
       "4        False\n",
       "5        False\n",
       "6        False\n",
       "7        False\n",
       "8        False\n",
       "9        False\n",
       "10       False\n",
       "11       False\n",
       "12       False\n",
       "13       False\n",
       "14       False\n",
       "15       False\n",
       "16       False\n",
       "17       False\n",
       "18       False\n",
       "19       False\n",
       "20       False\n",
       "21       False\n",
       "22       False\n",
       "23       False\n",
       "24       False\n",
       "25       False\n",
       "26       False\n",
       "27       False\n",
       "28       False\n",
       "29       False\n",
       "         ...  \n",
       "35519     True\n",
       "35520     True\n",
       "35521     True\n",
       "35522     True\n",
       "35523     True\n",
       "35524     True\n",
       "35525     True\n",
       "35526     True\n",
       "35527     True\n",
       "35528     True\n",
       "35529     True\n",
       "35530     True\n",
       "35531     True\n",
       "35532     True\n",
       "35533     True\n",
       "35534     True\n",
       "35535     True\n",
       "35536     True\n",
       "35537     True\n",
       "35538     True\n",
       "35539     True\n",
       "35540     True\n",
       "35541     True\n",
       "35542     True\n",
       "35543     True\n",
       "35544     True\n",
       "35545     True\n",
       "35546     True\n",
       "35547     True\n",
       "35548     True\n",
       "Name: year, dtype: bool"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "surveys_df.year == 2002"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>record_id</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>year</th>\n",
       "      <th>plot_id</th>\n",
       "      <th>species_id</th>\n",
       "      <th>sex</th>\n",
       "      <th>hindfoot_length</th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "      <td>1977</td>\n",
       "      <td>2</td>\n",
       "      <td>NL</td>\n",
       "      <td>M</td>\n",
       "      <td>32.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "      <td>1977</td>\n",
       "      <td>3</td>\n",
       "      <td>NL</td>\n",
       "      <td>M</td>\n",
       "      <td>33.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "      <td>1977</td>\n",
       "      <td>2</td>\n",
       "      <td>DM</td>\n",
       "      <td>F</td>\n",
       "      <td>37.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "      <td>1977</td>\n",
       "      <td>7</td>\n",
       "      <td>DM</td>\n",
       "      <td>M</td>\n",
       "      <td>36.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "      <td>1977</td>\n",
       "      <td>3</td>\n",
       "      <td>DM</td>\n",
       "      <td>M</td>\n",
       "      <td>35.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "      <td>1977</td>\n",
       "      <td>1</td>\n",
       "      <td>PF</td>\n",
       "      <td>M</td>\n",
       "      <td>14.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "      <td>1977</td>\n",
       "      <td>2</td>\n",
       "      <td>PE</td>\n",
       "      <td>F</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "      <td>1977</td>\n",
       "      <td>1</td>\n",
       "      <td>DM</td>\n",
       "      <td>M</td>\n",
       "      <td>37.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "      <td>1977</td>\n",
       "      <td>1</td>\n",
       "      <td>DM</td>\n",
       "      <td>F</td>\n",
       "      <td>34.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "      <td>1977</td>\n",
       "      <td>6</td>\n",
       "      <td>PF</td>\n",
       "      <td>F</td>\n",
       "      <td>20.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "      <td>1977</td>\n",
       "      <td>5</td>\n",
       "      <td>DS</td>\n",
       "      <td>F</td>\n",
       "      <td>53.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "      <td>1977</td>\n",
       "      <td>7</td>\n",
       "      <td>DM</td>\n",
       "      <td>M</td>\n",
       "      <td>38.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "      <td>1977</td>\n",
       "      <td>3</td>\n",
       "      <td>DM</td>\n",
       "      <td>M</td>\n",
       "      <td>35.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "      <td>1977</td>\n",
       "      <td>8</td>\n",
       "      <td>DM</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "      <td>1977</td>\n",
       "      <td>6</td>\n",
       "      <td>DM</td>\n",
       "      <td>F</td>\n",
       "      <td>36.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "      <td>1977</td>\n",
       "      <td>4</td>\n",
       "      <td>DM</td>\n",
       "      <td>F</td>\n",
       "      <td>36.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "      <td>1977</td>\n",
       "      <td>3</td>\n",
       "      <td>DS</td>\n",
       "      <td>F</td>\n",
       "      <td>48.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "      <td>1977</td>\n",
       "      <td>2</td>\n",
       "      <td>PP</td>\n",
       "      <td>M</td>\n",
       "      <td>22.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "      <td>1977</td>\n",
       "      <td>4</td>\n",
       "      <td>PF</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>7</td>\n",
       "      <td>17</td>\n",
       "      <td>1977</td>\n",
       "      <td>11</td>\n",
       "      <td>DS</td>\n",
       "      <td>F</td>\n",
       "      <td>48.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>7</td>\n",
       "      <td>17</td>\n",
       "      <td>1977</td>\n",
       "      <td>14</td>\n",
       "      <td>DM</td>\n",
       "      <td>F</td>\n",
       "      <td>34.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>7</td>\n",
       "      <td>17</td>\n",
       "      <td>1977</td>\n",
       "      <td>15</td>\n",
       "      <td>NL</td>\n",
       "      <td>F</td>\n",
       "      <td>31.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>7</td>\n",
       "      <td>17</td>\n",
       "      <td>1977</td>\n",
       "      <td>13</td>\n",
       "      <td>DM</td>\n",
       "      <td>M</td>\n",
       "      <td>36.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>7</td>\n",
       "      <td>17</td>\n",
       "      <td>1977</td>\n",
       "      <td>13</td>\n",
       "      <td>SH</td>\n",
       "      <td>M</td>\n",
       "      <td>21.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>7</td>\n",
       "      <td>17</td>\n",
       "      <td>1977</td>\n",
       "      <td>9</td>\n",
       "      <td>DM</td>\n",
       "      <td>M</td>\n",
       "      <td>35.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>7</td>\n",
       "      <td>17</td>\n",
       "      <td>1977</td>\n",
       "      <td>15</td>\n",
       "      <td>DM</td>\n",
       "      <td>M</td>\n",
       "      <td>31.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>7</td>\n",
       "      <td>17</td>\n",
       "      <td>1977</td>\n",
       "      <td>15</td>\n",
       "      <td>DM</td>\n",
       "      <td>M</td>\n",
       "      <td>36.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>7</td>\n",
       "      <td>17</td>\n",
       "      <td>1977</td>\n",
       "      <td>11</td>\n",
       "      <td>DM</td>\n",
       "      <td>M</td>\n",
       "      <td>38.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>7</td>\n",
       "      <td>17</td>\n",
       "      <td>1977</td>\n",
       "      <td>11</td>\n",
       "      <td>PP</td>\n",
       "      <td>M</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>7</td>\n",
       "      <td>17</td>\n",
       "      <td>1977</td>\n",
       "      <td>10</td>\n",
       "      <td>DS</td>\n",
       "      <td>F</td>\n",
       "      <td>52.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33290</th>\n",
       "      <td>33291</td>\n",
       "      <td>12</td>\n",
       "      <td>15</td>\n",
       "      <td>2001</td>\n",
       "      <td>23</td>\n",
       "      <td>PE</td>\n",
       "      <td>M</td>\n",
       "      <td>20.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33291</th>\n",
       "      <td>33292</td>\n",
       "      <td>12</td>\n",
       "      <td>15</td>\n",
       "      <td>2001</td>\n",
       "      <td>23</td>\n",
       "      <td>RM</td>\n",
       "      <td>F</td>\n",
       "      <td>16.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33292</th>\n",
       "      <td>33293</td>\n",
       "      <td>12</td>\n",
       "      <td>15</td>\n",
       "      <td>2001</td>\n",
       "      <td>20</td>\n",
       "      <td>PE</td>\n",
       "      <td>F</td>\n",
       "      <td>20.0</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33293</th>\n",
       "      <td>33294</td>\n",
       "      <td>12</td>\n",
       "      <td>15</td>\n",
       "      <td>2001</td>\n",
       "      <td>20</td>\n",
       "      <td>SH</td>\n",
       "      <td>M</td>\n",
       "      <td>25.0</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33294</th>\n",
       "      <td>33295</td>\n",
       "      <td>12</td>\n",
       "      <td>15</td>\n",
       "      <td>2001</td>\n",
       "      <td>20</td>\n",
       "      <td>PB</td>\n",
       "      <td>F</td>\n",
       "      <td>27.0</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33295</th>\n",
       "      <td>33296</td>\n",
       "      <td>12</td>\n",
       "      <td>15</td>\n",
       "      <td>2001</td>\n",
       "      <td>20</td>\n",
       "      <td>PB</td>\n",
       "      <td>M</td>\n",
       "      <td>25.0</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33296</th>\n",
       "      <td>33297</td>\n",
       "      <td>12</td>\n",
       "      <td>15</td>\n",
       "      <td>2001</td>\n",
       "      <td>20</td>\n",
       "      <td>RM</td>\n",
       "      <td>M</td>\n",
       "      <td>16.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33297</th>\n",
       "      <td>33298</td>\n",
       "      <td>12</td>\n",
       "      <td>15</td>\n",
       "      <td>2001</td>\n",
       "      <td>20</td>\n",
       "      <td>RM</td>\n",
       "      <td>F</td>\n",
       "      <td>16.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33298</th>\n",
       "      <td>33299</td>\n",
       "      <td>12</td>\n",
       "      <td>15</td>\n",
       "      <td>2001</td>\n",
       "      <td>20</td>\n",
       "      <td>PB</td>\n",
       "      <td>F</td>\n",
       "      <td>25.0</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33299</th>\n",
       "      <td>33300</td>\n",
       "      <td>12</td>\n",
       "      <td>15</td>\n",
       "      <td>2001</td>\n",
       "      <td>20</td>\n",
       "      <td>PB</td>\n",
       "      <td>F</td>\n",
       "      <td>26.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33300</th>\n",
       "      <td>33301</td>\n",
       "      <td>12</td>\n",
       "      <td>15</td>\n",
       "      <td>2001</td>\n",
       "      <td>20</td>\n",
       "      <td>PB</td>\n",
       "      <td>F</td>\n",
       "      <td>27.0</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33301</th>\n",
       "      <td>33302</td>\n",
       "      <td>12</td>\n",
       "      <td>15</td>\n",
       "      <td>2001</td>\n",
       "      <td>24</td>\n",
       "      <td>PE</td>\n",
       "      <td>M</td>\n",
       "      <td>20.0</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33302</th>\n",
       "      <td>33303</td>\n",
       "      <td>12</td>\n",
       "      <td>15</td>\n",
       "      <td>2001</td>\n",
       "      <td>24</td>\n",
       "      <td>PE</td>\n",
       "      <td>M</td>\n",
       "      <td>20.0</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33303</th>\n",
       "      <td>33304</td>\n",
       "      <td>12</td>\n",
       "      <td>15</td>\n",
       "      <td>2001</td>\n",
       "      <td>24</td>\n",
       "      <td>RM</td>\n",
       "      <td>M</td>\n",
       "      <td>16.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33304</th>\n",
       "      <td>33305</td>\n",
       "      <td>12</td>\n",
       "      <td>15</td>\n",
       "      <td>2001</td>\n",
       "      <td>7</td>\n",
       "      <td>PB</td>\n",
       "      <td>M</td>\n",
       "      <td>29.0</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33305</th>\n",
       "      <td>33306</td>\n",
       "      <td>12</td>\n",
       "      <td>15</td>\n",
       "      <td>2001</td>\n",
       "      <td>7</td>\n",
       "      <td>OT</td>\n",
       "      <td>M</td>\n",
       "      <td>19.0</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33306</th>\n",
       "      <td>33307</td>\n",
       "      <td>12</td>\n",
       "      <td>15</td>\n",
       "      <td>2001</td>\n",
       "      <td>7</td>\n",
       "      <td>OT</td>\n",
       "      <td>M</td>\n",
       "      <td>20.0</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33307</th>\n",
       "      <td>33308</td>\n",
       "      <td>12</td>\n",
       "      <td>15</td>\n",
       "      <td>2001</td>\n",
       "      <td>7</td>\n",
       "      <td>PP</td>\n",
       "      <td>M</td>\n",
       "      <td>24.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33308</th>\n",
       "      <td>33309</td>\n",
       "      <td>12</td>\n",
       "      <td>16</td>\n",
       "      <td>2001</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33309</th>\n",
       "      <td>33310</td>\n",
       "      <td>12</td>\n",
       "      <td>16</td>\n",
       "      <td>2001</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33310</th>\n",
       "      <td>33311</td>\n",
       "      <td>12</td>\n",
       "      <td>16</td>\n",
       "      <td>2001</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33311</th>\n",
       "      <td>33312</td>\n",
       "      <td>12</td>\n",
       "      <td>16</td>\n",
       "      <td>2001</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33312</th>\n",
       "      <td>33313</td>\n",
       "      <td>12</td>\n",
       "      <td>16</td>\n",
       "      <td>2001</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33313</th>\n",
       "      <td>33314</td>\n",
       "      <td>12</td>\n",
       "      <td>16</td>\n",
       "      <td>2001</td>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33314</th>\n",
       "      <td>33315</td>\n",
       "      <td>12</td>\n",
       "      <td>16</td>\n",
       "      <td>2001</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33315</th>\n",
       "      <td>33316</td>\n",
       "      <td>12</td>\n",
       "      <td>16</td>\n",
       "      <td>2001</td>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33316</th>\n",
       "      <td>33317</td>\n",
       "      <td>12</td>\n",
       "      <td>16</td>\n",
       "      <td>2001</td>\n",
       "      <td>13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33317</th>\n",
       "      <td>33318</td>\n",
       "      <td>12</td>\n",
       "      <td>16</td>\n",
       "      <td>2001</td>\n",
       "      <td>14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33318</th>\n",
       "      <td>33319</td>\n",
       "      <td>12</td>\n",
       "      <td>16</td>\n",
       "      <td>2001</td>\n",
       "      <td>15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33319</th>\n",
       "      <td>33320</td>\n",
       "      <td>12</td>\n",
       "      <td>16</td>\n",
       "      <td>2001</td>\n",
       "      <td>16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>33320 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       record_id  month  day  year  plot_id species_id  sex  hindfoot_length  \\\n",
       "0              1      7   16  1977        2         NL    M             32.0   \n",
       "1              2      7   16  1977        3         NL    M             33.0   \n",
       "2              3      7   16  1977        2         DM    F             37.0   \n",
       "3              4      7   16  1977        7         DM    M             36.0   \n",
       "4              5      7   16  1977        3         DM    M             35.0   \n",
       "5              6      7   16  1977        1         PF    M             14.0   \n",
       "6              7      7   16  1977        2         PE    F              NaN   \n",
       "7              8      7   16  1977        1         DM    M             37.0   \n",
       "8              9      7   16  1977        1         DM    F             34.0   \n",
       "9             10      7   16  1977        6         PF    F             20.0   \n",
       "10            11      7   16  1977        5         DS    F             53.0   \n",
       "11            12      7   16  1977        7         DM    M             38.0   \n",
       "12            13      7   16  1977        3         DM    M             35.0   \n",
       "13            14      7   16  1977        8         DM  NaN              NaN   \n",
       "14            15      7   16  1977        6         DM    F             36.0   \n",
       "15            16      7   16  1977        4         DM    F             36.0   \n",
       "16            17      7   16  1977        3         DS    F             48.0   \n",
       "17            18      7   16  1977        2         PP    M             22.0   \n",
       "18            19      7   16  1977        4         PF  NaN              NaN   \n",
       "19            20      7   17  1977       11         DS    F             48.0   \n",
       "20            21      7   17  1977       14         DM    F             34.0   \n",
       "21            22      7   17  1977       15         NL    F             31.0   \n",
       "22            23      7   17  1977       13         DM    M             36.0   \n",
       "23            24      7   17  1977       13         SH    M             21.0   \n",
       "24            25      7   17  1977        9         DM    M             35.0   \n",
       "25            26      7   17  1977       15         DM    M             31.0   \n",
       "26            27      7   17  1977       15         DM    M             36.0   \n",
       "27            28      7   17  1977       11         DM    M             38.0   \n",
       "28            29      7   17  1977       11         PP    M              NaN   \n",
       "29            30      7   17  1977       10         DS    F             52.0   \n",
       "...          ...    ...  ...   ...      ...        ...  ...              ...   \n",
       "33290      33291     12   15  2001       23         PE    M             20.0   \n",
       "33291      33292     12   15  2001       23         RM    F             16.0   \n",
       "33292      33293     12   15  2001       20         PE    F             20.0   \n",
       "33293      33294     12   15  2001       20         SH    M             25.0   \n",
       "33294      33295     12   15  2001       20         PB    F             27.0   \n",
       "33295      33296     12   15  2001       20         PB    M             25.0   \n",
       "33296      33297     12   15  2001       20         RM    M             16.0   \n",
       "33297      33298     12   15  2001       20         RM    F             16.0   \n",
       "33298      33299     12   15  2001       20         PB    F             25.0   \n",
       "33299      33300     12   15  2001       20         PB    F             26.0   \n",
       "33300      33301     12   15  2001       20         PB    F             27.0   \n",
       "33301      33302     12   15  2001       24         PE    M             20.0   \n",
       "33302      33303     12   15  2001       24         PE    M             20.0   \n",
       "33303      33304     12   15  2001       24         RM    M             16.0   \n",
       "33304      33305     12   15  2001        7         PB    M             29.0   \n",
       "33305      33306     12   15  2001        7         OT    M             19.0   \n",
       "33306      33307     12   15  2001        7         OT    M             20.0   \n",
       "33307      33308     12   15  2001        7         PP    M             24.0   \n",
       "33308      33309     12   16  2001        3        NaN  NaN              NaN   \n",
       "33309      33310     12   16  2001        4        NaN  NaN              NaN   \n",
       "33310      33311     12   16  2001        5        NaN  NaN              NaN   \n",
       "33311      33312     12   16  2001        6        NaN  NaN              NaN   \n",
       "33312      33313     12   16  2001        8        NaN  NaN              NaN   \n",
       "33313      33314     12   16  2001        9        NaN  NaN              NaN   \n",
       "33314      33315     12   16  2001       10        NaN  NaN              NaN   \n",
       "33315      33316     12   16  2001       11        NaN  NaN              NaN   \n",
       "33316      33317     12   16  2001       13        NaN  NaN              NaN   \n",
       "33317      33318     12   16  2001       14        NaN  NaN              NaN   \n",
       "33318      33319     12   16  2001       15        NaN  NaN              NaN   \n",
       "33319      33320     12   16  2001       16        NaN  NaN              NaN   \n",
       "\n",
       "       weight  \n",
       "0         NaN  \n",
       "1         NaN  \n",
       "2         NaN  \n",
       "3         NaN  \n",
       "4         NaN  \n",
       "5         NaN  \n",
       "6         NaN  \n",
       "7         NaN  \n",
       "8         NaN  \n",
       "9         NaN  \n",
       "10        NaN  \n",
       "11        NaN  \n",
       "12        NaN  \n",
       "13        NaN  \n",
       "14        NaN  \n",
       "15        NaN  \n",
       "16        NaN  \n",
       "17        NaN  \n",
       "18        NaN  \n",
       "19        NaN  \n",
       "20        NaN  \n",
       "21        NaN  \n",
       "22        NaN  \n",
       "23        NaN  \n",
       "24        NaN  \n",
       "25        NaN  \n",
       "26        NaN  \n",
       "27        NaN  \n",
       "28        NaN  \n",
       "29        NaN  \n",
       "...       ...  \n",
       "33290    18.0  \n",
       "33291     8.0  \n",
       "33292    22.0  \n",
       "33293    43.0  \n",
       "33294    33.0  \n",
       "33295    35.0  \n",
       "33296    11.0  \n",
       "33297     8.0  \n",
       "33298    28.0  \n",
       "33299    30.0  \n",
       "33300    31.0  \n",
       "33301    24.0  \n",
       "33302    23.0  \n",
       "33303    10.0  \n",
       "33304    44.0  \n",
       "33305    21.0  \n",
       "33306    19.0  \n",
       "33307    16.0  \n",
       "33308     NaN  \n",
       "33309     NaN  \n",
       "33310     NaN  \n",
       "33311     NaN  \n",
       "33312     NaN  \n",
       "33313     NaN  \n",
       "33314     NaN  \n",
       "33315     NaN  \n",
       "33316     NaN  \n",
       "33317     NaN  \n",
       "33318     NaN  \n",
       "33319     NaN  \n",
       "\n",
       "[33320 rows x 9 columns]"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "surveys_df[surveys_df.year != 2002]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>record_id</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>year</th>\n",
       "      <th>plot_id</th>\n",
       "      <th>species_id</th>\n",
       "      <th>sex</th>\n",
       "      <th>hindfoot_length</th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "      <td>1977</td>\n",
       "      <td>2</td>\n",
       "      <td>NL</td>\n",
       "      <td>M</td>\n",
       "      <td>32.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "      <td>1977</td>\n",
       "      <td>3</td>\n",
       "      <td>NL</td>\n",
       "      <td>M</td>\n",
       "      <td>33.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "      <td>1977</td>\n",
       "      <td>7</td>\n",
       "      <td>DM</td>\n",
       "      <td>M</td>\n",
       "      <td>36.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "      <td>1977</td>\n",
       "      <td>3</td>\n",
       "      <td>DM</td>\n",
       "      <td>M</td>\n",
       "      <td>35.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "      <td>1977</td>\n",
       "      <td>1</td>\n",
       "      <td>PF</td>\n",
       "      <td>M</td>\n",
       "      <td>14.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "      <td>1977</td>\n",
       "      <td>1</td>\n",
       "      <td>DM</td>\n",
       "      <td>M</td>\n",
       "      <td>37.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "      <td>1977</td>\n",
       "      <td>7</td>\n",
       "      <td>DM</td>\n",
       "      <td>M</td>\n",
       "      <td>38.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "      <td>1977</td>\n",
       "      <td>3</td>\n",
       "      <td>DM</td>\n",
       "      <td>M</td>\n",
       "      <td>35.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "      <td>1977</td>\n",
       "      <td>2</td>\n",
       "      <td>PP</td>\n",
       "      <td>M</td>\n",
       "      <td>22.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>7</td>\n",
       "      <td>17</td>\n",
       "      <td>1977</td>\n",
       "      <td>13</td>\n",
       "      <td>DM</td>\n",
       "      <td>M</td>\n",
       "      <td>36.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>7</td>\n",
       "      <td>17</td>\n",
       "      <td>1977</td>\n",
       "      <td>13</td>\n",
       "      <td>SH</td>\n",
       "      <td>M</td>\n",
       "      <td>21.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>7</td>\n",
       "      <td>17</td>\n",
       "      <td>1977</td>\n",
       "      <td>9</td>\n",
       "      <td>DM</td>\n",
       "      <td>M</td>\n",
       "      <td>35.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>7</td>\n",
       "      <td>17</td>\n",
       "      <td>1977</td>\n",
       "      <td>15</td>\n",
       "      <td>DM</td>\n",
       "      <td>M</td>\n",
       "      <td>31.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>7</td>\n",
       "      <td>17</td>\n",
       "      <td>1977</td>\n",
       "      <td>15</td>\n",
       "      <td>DM</td>\n",
       "      <td>M</td>\n",
       "      <td>36.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>7</td>\n",
       "      <td>17</td>\n",
       "      <td>1977</td>\n",
       "      <td>11</td>\n",
       "      <td>DM</td>\n",
       "      <td>M</td>\n",
       "      <td>38.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>7</td>\n",
       "      <td>17</td>\n",
       "      <td>1977</td>\n",
       "      <td>11</td>\n",
       "      <td>PP</td>\n",
       "      <td>M</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>38</td>\n",
       "      <td>7</td>\n",
       "      <td>17</td>\n",
       "      <td>1977</td>\n",
       "      <td>17</td>\n",
       "      <td>NL</td>\n",
       "      <td>M</td>\n",
       "      <td>33.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>40</td>\n",
       "      <td>7</td>\n",
       "      <td>18</td>\n",
       "      <td>1977</td>\n",
       "      <td>20</td>\n",
       "      <td>DM</td>\n",
       "      <td>M</td>\n",
       "      <td>36.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>43</td>\n",
       "      <td>7</td>\n",
       "      <td>18</td>\n",
       "      <td>1977</td>\n",
       "      <td>21</td>\n",
       "      <td>DM</td>\n",
       "      <td>M</td>\n",
       "      <td>36.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>45</td>\n",
       "      <td>7</td>\n",
       "      <td>18</td>\n",
       "      <td>1977</td>\n",
       "      <td>22</td>\n",
       "      <td>DM</td>\n",
       "      <td>M</td>\n",
       "      <td>36.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>46</td>\n",
       "      <td>7</td>\n",
       "      <td>18</td>\n",
       "      <td>1977</td>\n",
       "      <td>19</td>\n",
       "      <td>DM</td>\n",
       "      <td>M</td>\n",
       "      <td>35.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>47</td>\n",
       "      <td>7</td>\n",
       "      <td>18</td>\n",
       "      <td>1977</td>\n",
       "      <td>18</td>\n",
       "      <td>DM</td>\n",
       "      <td>M</td>\n",
       "      <td>32.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>53</td>\n",
       "      <td>7</td>\n",
       "      <td>18</td>\n",
       "      <td>1977</td>\n",
       "      <td>22</td>\n",
       "      <td>DM</td>\n",
       "      <td>M</td>\n",
       "      <td>36.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>54</td>\n",
       "      <td>7</td>\n",
       "      <td>18</td>\n",
       "      <td>1977</td>\n",
       "      <td>18</td>\n",
       "      <td>DM</td>\n",
       "      <td>M</td>\n",
       "      <td>37.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>55</td>\n",
       "      <td>7</td>\n",
       "      <td>18</td>\n",
       "      <td>1977</td>\n",
       "      <td>23</td>\n",
       "      <td>DM</td>\n",
       "      <td>M</td>\n",
       "      <td>36.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>56</td>\n",
       "      <td>7</td>\n",
       "      <td>18</td>\n",
       "      <td>1977</td>\n",
       "      <td>20</td>\n",
       "      <td>DM</td>\n",
       "      <td>M</td>\n",
       "      <td>34.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>58</td>\n",
       "      <td>7</td>\n",
       "      <td>18</td>\n",
       "      <td>1977</td>\n",
       "      <td>12</td>\n",
       "      <td>DS</td>\n",
       "      <td>M</td>\n",
       "      <td>45.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>59</td>\n",
       "      <td>7</td>\n",
       "      <td>18</td>\n",
       "      <td>1977</td>\n",
       "      <td>19</td>\n",
       "      <td>DM</td>\n",
       "      <td>M</td>\n",
       "      <td>33.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>60</td>\n",
       "      <td>7</td>\n",
       "      <td>18</td>\n",
       "      <td>1977</td>\n",
       "      <td>19</td>\n",
       "      <td>PE</td>\n",
       "      <td>M</td>\n",
       "      <td>20.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>61</td>\n",
       "      <td>7</td>\n",
       "      <td>18</td>\n",
       "      <td>1977</td>\n",
       "      <td>23</td>\n",
       "      <td>DM</td>\n",
       "      <td>M</td>\n",
       "      <td>35.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11168</th>\n",
       "      <td>11169</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>1985</td>\n",
       "      <td>8</td>\n",
       "      <td>DS</td>\n",
       "      <td>M</td>\n",
       "      <td>50.0</td>\n",
       "      <td>148.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11169</th>\n",
       "      <td>11170</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>1985</td>\n",
       "      <td>4</td>\n",
       "      <td>DM</td>\n",
       "      <td>M</td>\n",
       "      <td>38.0</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11172</th>\n",
       "      <td>11173</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>1985</td>\n",
       "      <td>9</td>\n",
       "      <td>DM</td>\n",
       "      <td>M</td>\n",
       "      <td>36.0</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11173</th>\n",
       "      <td>11174</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>1985</td>\n",
       "      <td>3</td>\n",
       "      <td>NL</td>\n",
       "      <td>M</td>\n",
       "      <td>33.0</td>\n",
       "      <td>225.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11174</th>\n",
       "      <td>11175</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>1985</td>\n",
       "      <td>11</td>\n",
       "      <td>DM</td>\n",
       "      <td>M</td>\n",
       "      <td>35.0</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11175</th>\n",
       "      <td>11176</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>1985</td>\n",
       "      <td>11</td>\n",
       "      <td>RM</td>\n",
       "      <td>M</td>\n",
       "      <td>17.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11177</th>\n",
       "      <td>11178</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>1985</td>\n",
       "      <td>1</td>\n",
       "      <td>DS</td>\n",
       "      <td>M</td>\n",
       "      <td>51.0</td>\n",
       "      <td>155.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11178</th>\n",
       "      <td>11179</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>1985</td>\n",
       "      <td>11</td>\n",
       "      <td>DO</td>\n",
       "      <td>M</td>\n",
       "      <td>35.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11180</th>\n",
       "      <td>11181</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>1985</td>\n",
       "      <td>13</td>\n",
       "      <td>DS</td>\n",
       "      <td>M</td>\n",
       "      <td>52.0</td>\n",
       "      <td>153.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11181</th>\n",
       "      <td>11182</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>1985</td>\n",
       "      <td>5</td>\n",
       "      <td>DO</td>\n",
       "      <td>M</td>\n",
       "      <td>36.0</td>\n",
       "      <td>55.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11182</th>\n",
       "      <td>11183</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>1985</td>\n",
       "      <td>11</td>\n",
       "      <td>DM</td>\n",
       "      <td>M</td>\n",
       "      <td>38.0</td>\n",
       "      <td>42.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11183</th>\n",
       "      <td>11184</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>1985</td>\n",
       "      <td>13</td>\n",
       "      <td>DM</td>\n",
       "      <td>M</td>\n",
       "      <td>38.0</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11185</th>\n",
       "      <td>11186</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>1985</td>\n",
       "      <td>2</td>\n",
       "      <td>DM</td>\n",
       "      <td>M</td>\n",
       "      <td>38.0</td>\n",
       "      <td>42.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11186</th>\n",
       "      <td>11187</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>1985</td>\n",
       "      <td>2</td>\n",
       "      <td>DM</td>\n",
       "      <td>M</td>\n",
       "      <td>36.0</td>\n",
       "      <td>48.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11188</th>\n",
       "      <td>11189</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>1985</td>\n",
       "      <td>8</td>\n",
       "      <td>DM</td>\n",
       "      <td>M</td>\n",
       "      <td>36.0</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11189</th>\n",
       "      <td>11190</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>1985</td>\n",
       "      <td>5</td>\n",
       "      <td>DO</td>\n",
       "      <td>M</td>\n",
       "      <td>35.0</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11190</th>\n",
       "      <td>11191</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>1985</td>\n",
       "      <td>9</td>\n",
       "      <td>DM</td>\n",
       "      <td>M</td>\n",
       "      <td>36.0</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11194</th>\n",
       "      <td>11195</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>1985</td>\n",
       "      <td>1</td>\n",
       "      <td>DM</td>\n",
       "      <td>M</td>\n",
       "      <td>35.0</td>\n",
       "      <td>39.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11195</th>\n",
       "      <td>11196</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>1985</td>\n",
       "      <td>13</td>\n",
       "      <td>OL</td>\n",
       "      <td>M</td>\n",
       "      <td>20.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11197</th>\n",
       "      <td>11198</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>1985</td>\n",
       "      <td>4</td>\n",
       "      <td>DS</td>\n",
       "      <td>M</td>\n",
       "      <td>45.0</td>\n",
       "      <td>129.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11200</th>\n",
       "      <td>11201</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>1985</td>\n",
       "      <td>5</td>\n",
       "      <td>OL</td>\n",
       "      <td>M</td>\n",
       "      <td>21.0</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11204</th>\n",
       "      <td>11205</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>1985</td>\n",
       "      <td>5</td>\n",
       "      <td>DO</td>\n",
       "      <td>M</td>\n",
       "      <td>37.0</td>\n",
       "      <td>56.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11206</th>\n",
       "      <td>11207</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>1985</td>\n",
       "      <td>2</td>\n",
       "      <td>PE</td>\n",
       "      <td>M</td>\n",
       "      <td>18.0</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11210</th>\n",
       "      <td>11211</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>1985</td>\n",
       "      <td>13</td>\n",
       "      <td>DM</td>\n",
       "      <td>M</td>\n",
       "      <td>37.0</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11215</th>\n",
       "      <td>11216</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>1985</td>\n",
       "      <td>3</td>\n",
       "      <td>RM</td>\n",
       "      <td>M</td>\n",
       "      <td>17.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11216</th>\n",
       "      <td>11217</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>1985</td>\n",
       "      <td>4</td>\n",
       "      <td>OL</td>\n",
       "      <td>M</td>\n",
       "      <td>24.0</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11222</th>\n",
       "      <td>11223</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>1985</td>\n",
       "      <td>4</td>\n",
       "      <td>DM</td>\n",
       "      <td>M</td>\n",
       "      <td>36.0</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11223</th>\n",
       "      <td>11224</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>1985</td>\n",
       "      <td>11</td>\n",
       "      <td>DM</td>\n",
       "      <td>M</td>\n",
       "      <td>37.0</td>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11224</th>\n",
       "      <td>11225</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>1985</td>\n",
       "      <td>7</td>\n",
       "      <td>PE</td>\n",
       "      <td>M</td>\n",
       "      <td>20.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11225</th>\n",
       "      <td>11226</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>1985</td>\n",
       "      <td>1</td>\n",
       "      <td>DM</td>\n",
       "      <td>M</td>\n",
       "      <td>38.0</td>\n",
       "      <td>47.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5426 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       record_id  month  day  year  plot_id species_id sex  hindfoot_length  \\\n",
       "0              1      7   16  1977        2         NL   M             32.0   \n",
       "1              2      7   16  1977        3         NL   M             33.0   \n",
       "3              4      7   16  1977        7         DM   M             36.0   \n",
       "4              5      7   16  1977        3         DM   M             35.0   \n",
       "5              6      7   16  1977        1         PF   M             14.0   \n",
       "7              8      7   16  1977        1         DM   M             37.0   \n",
       "11            12      7   16  1977        7         DM   M             38.0   \n",
       "12            13      7   16  1977        3         DM   M             35.0   \n",
       "17            18      7   16  1977        2         PP   M             22.0   \n",
       "22            23      7   17  1977       13         DM   M             36.0   \n",
       "23            24      7   17  1977       13         SH   M             21.0   \n",
       "24            25      7   17  1977        9         DM   M             35.0   \n",
       "25            26      7   17  1977       15         DM   M             31.0   \n",
       "26            27      7   17  1977       15         DM   M             36.0   \n",
       "27            28      7   17  1977       11         DM   M             38.0   \n",
       "28            29      7   17  1977       11         PP   M              NaN   \n",
       "37            38      7   17  1977       17         NL   M             33.0   \n",
       "39            40      7   18  1977       20         DM   M             36.0   \n",
       "42            43      7   18  1977       21         DM   M             36.0   \n",
       "44            45      7   18  1977       22         DM   M             36.0   \n",
       "45            46      7   18  1977       19         DM   M             35.0   \n",
       "46            47      7   18  1977       18         DM   M             32.0   \n",
       "52            53      7   18  1977       22         DM   M             36.0   \n",
       "53            54      7   18  1977       18         DM   M             37.0   \n",
       "54            55      7   18  1977       23         DM   M             36.0   \n",
       "55            56      7   18  1977       20         DM   M             34.0   \n",
       "57            58      7   18  1977       12         DS   M             45.0   \n",
       "58            59      7   18  1977       19         DM   M             33.0   \n",
       "59            60      7   18  1977       19         PE   M             20.0   \n",
       "60            61      7   18  1977       23         DM   M             35.0   \n",
       "...          ...    ...  ...   ...      ...        ...  ..              ...   \n",
       "11168      11169     12    8  1985        8         DS   M             50.0   \n",
       "11169      11170     12    8  1985        4         DM   M             38.0   \n",
       "11172      11173     12    8  1985        9         DM   M             36.0   \n",
       "11173      11174     12    8  1985        3         NL   M             33.0   \n",
       "11174      11175     12    8  1985       11         DM   M             35.0   \n",
       "11175      11176     12    8  1985       11         RM   M             17.0   \n",
       "11177      11178     12    8  1985        1         DS   M             51.0   \n",
       "11178      11179     12    8  1985       11         DO   M             35.0   \n",
       "11180      11181     12    8  1985       13         DS   M             52.0   \n",
       "11181      11182     12    8  1985        5         DO   M             36.0   \n",
       "11182      11183     12    8  1985       11         DM   M             38.0   \n",
       "11183      11184     12    8  1985       13         DM   M             38.0   \n",
       "11185      11186     12    8  1985        2         DM   M             38.0   \n",
       "11186      11187     12    8  1985        2         DM   M             36.0   \n",
       "11188      11189     12    8  1985        8         DM   M             36.0   \n",
       "11189      11190     12    8  1985        5         DO   M             35.0   \n",
       "11190      11191     12    8  1985        9         DM   M             36.0   \n",
       "11194      11195     12    8  1985        1         DM   M             35.0   \n",
       "11195      11196     12    8  1985       13         OL   M             20.0   \n",
       "11197      11198     12    8  1985        4         DS   M             45.0   \n",
       "11200      11201     12    8  1985        5         OL   M             21.0   \n",
       "11204      11205     12    8  1985        5         DO   M             37.0   \n",
       "11206      11207     12    8  1985        2         PE   M             18.0   \n",
       "11210      11211     12    8  1985       13         DM   M             37.0   \n",
       "11215      11216     12    8  1985        3         RM   M             17.0   \n",
       "11216      11217     12    8  1985        4         OL   M             24.0   \n",
       "11222      11223     12    8  1985        4         DM   M             36.0   \n",
       "11223      11224     12    8  1985       11         DM   M             37.0   \n",
       "11224      11225     12    8  1985        7         PE   M             20.0   \n",
       "11225      11226     12    8  1985        1         DM   M             38.0   \n",
       "\n",
       "       weight  \n",
       "0         NaN  \n",
       "1         NaN  \n",
       "3         NaN  \n",
       "4         NaN  \n",
       "5         NaN  \n",
       "7         NaN  \n",
       "11        NaN  \n",
       "12        NaN  \n",
       "17        NaN  \n",
       "22        NaN  \n",
       "23        NaN  \n",
       "24        NaN  \n",
       "25        NaN  \n",
       "26        NaN  \n",
       "27        NaN  \n",
       "28        NaN  \n",
       "37        NaN  \n",
       "39        NaN  \n",
       "42        NaN  \n",
       "44        NaN  \n",
       "45        NaN  \n",
       "46        NaN  \n",
       "52        NaN  \n",
       "53        NaN  \n",
       "54        NaN  \n",
       "55        NaN  \n",
       "57        NaN  \n",
       "58        NaN  \n",
       "59        NaN  \n",
       "60        NaN  \n",
       "...       ...  \n",
       "11168   148.0  \n",
       "11169    45.0  \n",
       "11172    40.0  \n",
       "11173   225.0  \n",
       "11174    43.0  \n",
       "11175     9.0  \n",
       "11177   155.0  \n",
       "11178     NaN  \n",
       "11180   153.0  \n",
       "11181    55.0  \n",
       "11182    42.0  \n",
       "11183    43.0  \n",
       "11185    42.0  \n",
       "11186    48.0  \n",
       "11188    45.0  \n",
       "11189    50.0  \n",
       "11190    50.0  \n",
       "11194    39.0  \n",
       "11195    30.0  \n",
       "11197   129.0  \n",
       "11200    29.0  \n",
       "11204    56.0  \n",
       "11206    19.0  \n",
       "11210    45.0  \n",
       "11215     9.0  \n",
       "11216    34.0  \n",
       "11222    40.0  \n",
       "11223    49.0  \n",
       "11224    18.0  \n",
       "11225    47.0  \n",
       "\n",
       "[5426 rows x 9 columns]"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "surveys_df[(surveys_df.sex == 'M') & (surveys_df.year <= 1985)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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